PromptOps Reliability Science & Prompt Engineering Glossary (Hindi, English & Hinglish)
Part of the HCAM-KG™ Knowledge Graph · B-30 BHARAT AI Education Badge - Level 2
Prerequisite Signal (Important): This glossary is designed for B-30 BHARAT AI EDUCATION BADGE - Level 2 learners.Ideally, you should have completed Level 1 before starting this page.
Agar aapne Level 1 complete nahi kiya hai, pehle use finish karna strongly recommended hai - kyunki Level 2 assumes foundational AI vocabulary & concepts.
Complete Level 1 Here ➡️ B-30 BHARAT AI EDUCATION BADGE ➡️
This B-30 MasterKey™ AI Glossary (Level 2) is built for learners who are moving beyond basics - from understanding AI to engineering reliable, production-ready AI conversations.
It uses a Hindi → English → Hinglish cognitive flow, not for translation, but for thinking clarity, prompt precision, and real-world application.
Specially developed for Bharat learners under the Hinglish Cognitive Anchoring Model™ (HCAM™).
Here, the goal is not curiosity anymore -
the goal is control, reliability, and creator-level capability.
From Curiosity → Creation → Credible Outcomes
Most people believe “AI is complex.” The real truth is simpler:
👉 AI is not complex.
AI vocabulary feels complex.
Terms like PromptOps, Reliability, Guardrails, RAG, Agents, System Prompts, Evaluation Loops sound intimidating - not because they are hard, but because they are explained in foreign cognitive formats.
For Bharat learners, the real gap is:
- Mind thinks in Hinglish
- AI responds in English
- Most books teach in pure technical English
Result?
👉 Concept samajh aata hai, par words badalte hi confidence nahi aata.
That is not a technology problem.
That is a Vocabulary + Recall + Application gap.
Why Level 2 Is Different
Level 1 focused on AI Literacy.
Level 2 focuses on AI Reliability, PromptOps, and Production Thinking.
This glossary is designed to help you:
- Think clearly while prompting
- Design prompts that don’t break in real use
- Understand why prompts fail, drift, or hallucinate
- Stop being a passive user and become a PromptOps-aware creator
This is where “Machine ke saath baat karna” becomes....
👉 Machine ke saath kaam karna.
The HCAM™ Advantage (Why Hinglish Matters Here)
We do not translate terms. We anchor meaning across three layers:
- Hindi ➡️ clarity (samajh)
- English ➡️ precision (exact meaning)
- Hinglish ➡️ recall + application (real life use)
This is the Hinglish Cognitive Anchoring Model™:
Language-First, not Translation-First
Bharat ke liye sirf translation kaafi nahi hota -
Trans-creation zaroori hoti hai.
Simple science behind HCAM™:
➡️ Hindi clarity
➡️ English precision
➡️ Hinglish recall
➡️ Real-world application
➡️ Reinforced understanding
This locks vocabulary into long-term memory - not just for exams or reading, but for doing real AI work.
What This Glossary Is (and Is Not)
✅ This is a working glossary, not a reading glossary
✅ Built for PromptOps, Reliability Science, Multi-Agent thinking
✅ Designed for students, professionals, educators, builders
✅ Bharat-first, but globally relevant
❌ This is not a government certification
❌ Not part of SWAYAM, YUVA AI for ALL, or similar programs
It is an independent capability badge, developed by GurukulAI Thought Lab, aligned with the Augmented Workforce Paradigm™: AI Collaboration, not AI Replacement
The intent is to strengthen and accelerate the learning mindset behind national AI initiatives - not compete with them.
Why Vocabulary = Creator Advantage
AI is now everywhere:
Education, Finance, Marketing, Healthcare, Coding, Design, Operations - even daily life.
The rule is simple:
- Jitni strong aapki AI vocabulary,
- utni smooth aapki machine-conversation,
- aur utni powerful aapki creation.
Strong vocabulary = 🎯 Better prompts
🎯 Better outputs
🎯 Better trust
🎯 Better monetizable skills
You’re in the Right Place If…
- You don’t want surface-level AI tricks
- You want reliable, explainable, production-ready AI usage
- You want to move from user to co-creator
- You want AI clarity that actually converts into capability and income
What Happens Next
Now we stop theory.
Below starts the Level-2 PromptOps & Prompt Engineering Glossary -
a carefully structured set of terms that will:
- Strengthen your AI foundations
- Sharpen your prompting mindset
- Prepare you for advanced systems, agents, and real deployments
If Level 1 was about understanding AI,
Level 2 is about commanding it responsibly.
Ready?
Let’s begin with the vocabulary that gives you
clarity, control, and creator-level confidence in the AI era.
B-30 BHARAT AI EDUCATION BADGE - Level 2 Journey: Advanced PromptOps Reliability Science & Prompt Engineering
Optional
Advance Prompt Engineering
Context Window / प्रसंग विंडो (AI की अल्पकालिक स्मृति) WindowMind™
HINDI: Context Window (प्रसंग विंडो) वह सीमा है जितना टेक्स्ट/टोकन (tokens) AI एक समय में “एक्टिव” रूप से पढ़कर उपयोग कर सकता है। इसे AI की अल्पकालिक स्मृति मानिए - जो चीज़ें इस सीमा से बाहर चली जाती हैं, वे AI की working view में नहीं रहतीं। इसलिए लंबे prompts/लंबी chats में शुरू के नियम, facts, या constraints “छूट” सकते हैं। यही कारण है कि prompt का क्रम (ordering), सारांश/संक्षेप (compression), और chunking जैसी तकनीकें reliability के लिए जरूरी हैं।
ENGLISH: A context window is the fixed amount of text (tokens) an LLM can actively use at one time. If the conversation or document exceeds this limit, earlier details may drop out of the model’s working view. This is why prompt length, ordering, and compression matter for reliability and consistency.
HINGLISH: WindowMind™ AI ka dimaag ek “working whiteboard” jaisa hota hai - space limited. Tum jitna zyada ek saath chipkaoge, utna purana content whiteboard se mitne lagta hai.
Day-to-day example: WhatsApp me 300 msgs ke baad “upar wali baat follow karo” बोलो, सामने वाला भूल जाता है. AI bhi aise hi “active view” tak hi follow karta hai.
Anchor hook: “Whiteboard chhota = purani chalk gayab.”
Recall key: WindowMind = jitna dikhe, utna yaad.
Interview Intent Signals™:
1. What is a context window in an LLM, and how does it affect long conversations?
2. If an instruction at the start is being ignored later, what prompt strategies would you use to handle context window limits?
3. Explain why chunking and summarization improve reliability when working with long documents.
AssessmentIntent™:
In production prompt design, how would you prioritize information ordering inside limited context
What practical techniques reduce instruction loss due to context overflow (ordering, compression, chunking)
Priming / प्राइमिंग (शुरुआती निर्देशों का प्रभाव) FirstFrame™
HINDI: Priming (प्राइमिंग) का अर्थ है prompt की शुरुआत में दिए गए role/goal/tone/constraints AI के पूरे जवाब को दिशा देते हैं। शुरुआती 1–2 लाइनें AI के लिए “lens” सेट करती हैं, जिससे बाद की जानकारी उसी lens में interpret होती है। मजबूत priming से tone स्थिर रहता है, output की structure consistency बढ़ती है, और random drift घटता है।
ENGLISH: Priming is the effect where the earliest instructions (role, goal, context) influence how the model interprets everything that follows. Strong priming guides tone, priorities, and output structure more consistently. It is a practical control lever for reducing randomness in outputs.
HINGLISH: FirstFrame™ Priming मतलब “पहला frame तय करो.” Starting lines AI ko batati hain ki kis mode me kaam करना है. Agar start me clarity nahi, toh AI apna default generic mode le aata hai.
Day-to-day example: “Bhai seriously bol” कहने से दोस्त का tone बदल जाता है - AI ke saath bhi same.
Anchor hook: “First line = steering wheel.”
Recall key: FirstFrame = pehli line, poora vibe.
Framing / फ्रेमिंग (सवाल की भाषा से दिशा बदलना) AskShape™
HINDI: Framing (फ्रेमिंग) वह तकनीक है जिसमें आप एक ही विषय को अलग शब्दों/दृष्टिकोण से पूछकर AI के output का जोर बदल देते हैं। Frame positive/negative, deep/brief, neutral/biased किसी भी दिशा में push कर सकता है। Balanced framing bias कम करती है और decision-ready output देती है, जैसे trade-offs, assumptions, और risks शामिल करवाना।
ENGLISH: Framing is how wording and perspective change the model’s emphasis and direction, even when the topic stays the same. A frame can push outputs toward positives, negatives, depth, brevity, or neutrality. Good framing reduces bias and improves decision usefulness.
HINGLISH: AskShape™ Framing मतलब “sawaal ka shape.” Tum jaisa poochoge, AI usi angle se jawab देगा. Leading question doge toh one-sided output; balanced frame doge toh balanced output.
Day-to-day example: “Is product me problem kya hai?” vs “Pros + Cons dono बताओ” - answer quality बदल जाती है.
Anchor hook: “Question ka frame = answer ka frame.”
Recall key: AskShape = jaisa sawaal, waisa jawab.
AI as a Predictive Storyteller / AI एक पूर्वानुमान-आधारित कथाकार ProbabilisticNarrator™
HINDI: LLM शब्दों/टोकन का “अगला सबसे संभावित” अनुमान लगाकर टेक्स्ट बनाता है। इसलिए यह बेहद fluent और convincing लिख सकता है, लेकिन तथ्य हमेशा verify नहीं होते। Truth-critical काम में यह risk पैदा करता है क्योंकि AI confidence के साथ गलत चीज़ भी कह सकता है। इसीलिए grounding, source binding, retrieval, या verification steps जोड़ना जरूरी होता है।
ENGLISH: An LLM generates text by predicting likely next tokens based on patterns learned in training. It can create fluent, convincing narratives even when facts are unknown or unverified. This makes it powerful for creativity but risky for truth-critical tasks without grounding.
HINGLISH: ProbabilisticNarrator™ AI ek “smooth storyteller” hai - jo next word predict karke story banata hai. Smooth bolna = fact sahi hona nahi. Isliye factual tasks me prompt me proof + sources ka दबाव डालना पड़ता है.
Day-to-day example: Confident दोस्त गलत advice दे दे - सुनने में सही, reality me गलत.
Anchor hook: “Fluent =/= Fact.”
Recall key: Narrator = smooth bolta, proof nahi deta.
Zero-Shot Prompting / शून्य-उदाहरण प्रॉम्प्टिंग DirectAsk™
HINDI: Zero-shot prompting में आप बिना कोई example दिए सीधे task दे देते हैं। यह तेज़ है और idea exploration के लिए अच्छा है, लेकिन output में variability ज्यादा होती है क्योंकि AI को format/edge-cases “सिखाए” नहीं जाते। Production में इस्तेमाल करने से पहले constraints, examples, और checks जोड़ना बेहतर होता है।
ENGLISH: Zero-shot prompting assigns a task without providing examples. It is fast and useful for quick drafts, but results vary more because format and edge-case handling are not taught. It is best for exploration, not production reliability.
HINGLISH: DirectAsk™ Zero-shot मतलब “बस बोल दिया: कर do.” Speed मिलती है, लेकिन output कभी strong, कभी generic. Format नहीं दिया तो AI अपना default template चला देता है.
Day-to-day example: Intern को बोलो “report बना दो” - template नहीं दिया तो अलग-अलग style मिलेगा.
Anchor hook: “Example nahi, toh expectation loose.”
Recall key: DirectAsk = fast, but variable.
One-Shot Prompting / एक-उदाहरण प्रॉम्प्टिंग SinglePattern™
HINDI: One-shot prompting में आप desired output का सिर्फ एक example देते हैं ताकि AI उस pattern/format को mimic करे। इससे structure consistency बढ़ती है, लेकिन edge-cases में चूक हो सकती है क्योंकि एक example सभी विविधताओं को cover नहीं करता। Zero-shot की तुलना में ज्यादा stable, और Few-shot से कम token-cost वाला approach है।
ENGLISH: One-shot prompting provides one example of the desired output so the model follows a clearer structure. It improves format consistency but may fail on edge cases because one example rarely covers variety. It is a quick bridge between zero-shot and few-shot.
HINGLISH: SinglePattern™ Tum AI ko “ek नमूना” dikha do, woh उसी shape में बाकी output बना देगा. Par agar input variety ज्यादा है, ek sample कम पड़ सकता है.
Day-to-day example: Pehle एक सही email दिखाओ, फिर team उसी style में emails लिखती है.
Anchor hook: “One sample sets the mold.”
Recall key: SinglePattern = ek example, same format.
Few-Shot Prompting / बहु-उदाहरण प्रॉम्प्टिंग PatternTrainer™
HINDI: Few-shot prompting में आप कई examples देकर AI को classification/formatting/extraction का pattern “सिखाते” हैं। इससे consistency और reliability बढ़ती है, पर tokens ज्यादा लगते हैं और examples में bias हो तो output भी उसी bias को follow कर सकता है। इसलिए representative “golden” examples चुनना जरूरी है।
ENGLISH: Few-shot prompting provides multiple examples to teach the model a pattern for classification, formatting, or extraction. It increases consistency and reliability but consumes more context window tokens and can inherit biases present in the examples.
HINGLISH: PatternTrainer™ Multiple examples = AI ko training wheels मिलते हैं. Jitne better examples, utni better consistency. Lekin गलत examples doge toh AI “गलत pattern” सीख लेगा.
Day-to-day example: 5 सही solved sums देखकर student same type के sums solve करता है.
Anchor hook: “Examples teach behavior.”
Recall key: PatternTrainer = examples se pattern lock.
Role Prompting / भूमिका-आधारित प्रॉम्प्टिंग HatMode™
HINDI: Role prompting में आप AI को एक भूमिका (जैसे tutor, auditor, advisor) देते हैं ताकि tone, vocabulary और priorities उसी role के अनुसार align हों। यह education और simulations में प्रभावी है, पर अगर role “authority” imply करता है तो hallucination risk बढ़ सकता है। इसलिए boundaries और “अगर unsure हो तो बताओ” जैसे नियम जोड़ना जरूरी है।
ENGLISH: Role prompting assigns a persona (advisor, tutor, auditor) to shape tone, priorities, and vocabulary. It is effective for simulations, tutoring, and support, but can increase hallucination risk if the role implies authority beyond available knowledge.
HINGLISH: HatMode™ AI ko “hat” pehna do - tutor hat, auditor hat, friendly hat. Output तुरंत उसी posture में आ जाता है. Bas ध्यान रहे: role powerful है, पर limits भी lock करो.
Day-to-day example: Dost को “HR बनके बोल” बोलो, वो अलग language use करेगा.
Anchor hook: “Hat बदलो, जवाब बदलो.”
Recall key: HatMode = role switch, tone switch.
Instruction vs. Descriptive Prompting / निर्देश बनाम वर्णनात्मक प्रॉम्प्टिंग DoVsImagine™
HINDI: Instruction prompts सीधे बताते हैं “क्या करना है” - ये precision और repeatability के लिए best हैं। Descriptive prompts scene/कल्पना बनाते हैं, जो creativity बढ़ाते हैं। सही mode चुनने से drift कम होता है और output fit बेहतर होता है। Production में अक्सर instruction-first बेहतर रहता है, फिर जरूरत हो तो descriptive context जोड़ते हैं।
ENGLISH: Instruction prompts tell the model exactly what to do and are best for precision and repeatability. Descriptive prompts create a scene or imaginative context and are often better for creative ideation. Choosing the right mode reduces drift and improves output fit.
HINGLISH: DoVsImagine™ Instruction = “yeh karo” clarity. Descriptive = “socho aisa scene hai” creativity. Wrong choice से output या तो boring हो जाता है या off-track.
Day-to-day example: “2-page report लिखो” vs “CEO को impress करने वाली story बनाओ.”
Anchor hook: “Control vs creativity.”
Recall key: Do = control, Imagine = creative.
Hybrid Prompting / मिश्रित प्रॉम्प्टिंग BlendStack™
HINDI: Hybrid prompting में role + examples + constraints + evaluation जैसी multiple techniques एक साथ stack की जाती हैं ताकि output quality और consistency दोनों बढ़ें। Real-world में single-technique prompts edge-cases handle नहीं कर पाते, इसलिए production prompts अक्सर hybrid होते हैं। Hybrid design reliability के लिए “stacking” mindset बनाता है।
ENGLISH: Hybrid prompting combines multiple techniques - role, examples, constraints, and evaluation - to improve both quality and consistency. Most production prompts are hybrid because single techniques rarely handle real-world edge cases reliably.
HINGLISH: BlendStack™ Hybrid = ek hi prompt me multiple levers: role + examples + format rules + self-check. Isse output “stable” banta hai, demo nahi.
Day-to-day example: Recipe me सिर्फ namak नहीं, मसाले stack होते हैं तभी taste आता है.
Anchor hook: “Stack methods, stabilize results.”
Recall key: BlendStack = mix + lock.
F.O.R.M. Model / F.O.R.M. मॉडल (प्रॉम्प्ट कम्पास) FORM-Compass™
HINDI: FORM एक prompt checklist है: Format (आउटपुट कैसा चाहिए), Objective (क्या लक्ष्य है), Role (किस persona में), Method (कैसे सोचना/करना)। यह ambiguity घटाता है, जिससे output fragility कम होती है। FORM beginners के लिए भी prompt को professional structure देता है और team-level consistency बनाता है।
ENGLISH: FORM is a prompt checklist: Format, Objective, Role, Method. It forces clarity on output shape, task goal, voice/perspective, and reasoning style. FORM reduces ambiguity, which reduces fragility and inconsistency in responses.
HINGLISH: FORM-Compass™ FORM se prompt “clear brief” बनता है: output shape, goal, role, method - सब fixed. Jitni clarity, utni stability.
Day-to-day example: Client brief me format + goal clear हो तो काम smooth.
Anchor hook: “FORM = prompt का compass.”
Recall key: F-O-R-M = Format-Objective-Role-Method.
Summarization Prompts / सारांश प्रॉम्प्ट NoiseCutter™
HINDI: Summarization prompts लंबे टेक्स्ट को किसी specific audience के लिए compress करते हैं। अगर audience, focus areas, और “क्या ignore करना है” स्पष्ट नहीं होगा तो summary generic बन जाती है। Guardrails (length, bullets, exclusions) देने से सारांश decision-ready बनता है।
ENGLISH: Summarization prompts compress long text into key meaning for a specific audience. Output quality depends on constraints such as length, focus areas, and what to exclude. Without clear audience and priorities, summaries become generic and miss what matters.
HINGLISH: NoiseCutter™ Summary tab kaam ki hoti hai jab “kiske liye” aur “kis angle se” clear ho. Warna AI safe-generic bana deta hai.
Day-to-day example: CEO ko 5 bullets चाहिए, student ko detail चाहिए - same text, different summary.
Anchor hook: “Noise cut करो, signal रखो.”
Recall key: NoiseCutter = short, sharp, relevant.
Classification Prompts / वर्गीकरण प्रॉम्प्ट LabelLock™
HINDI: Classification prompts टेक्स्ट को predefined labels में map करते हैं। Labels की स्पष्ट definitions और examples देने से interpretation drift कम होता है। “Only label return करो” जैसी constraints routing systems में reliability बढ़ाती हैं और automation stable बनता है।
ENGLISH: Classification prompts map text into predefined labels. They work best when labels are clearly defined and examples are provided to reduce interpretation drift. Constraints like “return only the label” improve reliability in routing systems.
HINGLISH: LabelLock™ AI ko fixed buckets do - aur bolo “sirf bucket name लौटाओ.” Tab routing clean hota hai. Ambiguous cases ke liye examples जरूरी हैं.
Day-to-day example: Email sorting: Spam / Important / Normal.
Anchor hook: “Bucket clear, chaos कम.”
Recall key: LabelLock = label only output.
Extraction Prompts / निष्कर्षण प्रॉम्प्ट FieldMiner™
HINDI: Extraction prompts unstructured text से structured fields (table/JSON) निकालते हैं। समस्या तब होती है जब AI missing fields को guess करके भर देता है। इसलिए “N/A if missing” और strict schema rules जरूरी हैं ताकि hallucinated details कम हों और data reliable रहे।
ENGLISH: Extraction prompts convert unstructured text into structured fields (tables/JSON). They become unreliable when the model fills missing fields by guessing. Enforcing “N/A if missing” and strict schema output reduces hallucinated details.
HINGLISH: FieldMiner™ Extraction = text se fields nikaalna. AI ko साफ बोलो “अगर नहीं मिला तो N/A.” वरना वो “fill the blanks” खेल लेगा.
Day-to-day example: Invoice se Date/Amount निकालना. Missing हो तो blank/N-A.
Anchor hook: “Guess नहीं, extract.”
Recall key: FieldMiner = fields only, no guessing.
Translation Prompts / अनुवाद प्रॉम्प्ट ToneBridge™
HINDI: Translation prompts भाषा बदलते हुए meaning + tone + nuance बचाने पर ध्यान देते हैं। Literal translation अक्सर “अजीब/कठोर” लग सकता है और intent खो सकता है। इसलिए audience, formality level, और domain glossary constraints देना ज़रूरी है ताकि terms drift न हों।
ENGLISH: Translation prompts convert text between languages while preserving meaning, tone, and nuance. Literal translations can lose intent or sound unnatural. Specifying tone, audience, and cultural adaptation improves output usefulness in real communication.
HINGLISH: ToneBridge™ Translate karna = words नहीं, “meaning + vibe” shift करना. Tone specify nahi kiya toh output awkward हो सकता है. Domain terms के लिए glossary lock करो.
Day-to-day example: Privacy policy को “formal Hindi” में चाहिए, meme tone नहीं.
Anchor hook: “Words नहीं, vibe translate.”
Recall key: ToneBridge = meaning + tone transfer.
Creative Prompts / रचनात्मक प्रॉम्प्ट ImaginationRig™
HINDI: Creative prompts stories, scripts, campaigns जैसी imaginative outputs बनाते हैं। Constraints नहीं होंगे तो AI clichés और generic patterns में drift कर सकता है। Style, length, perspective, originality hooks, और “self-critique” जैसे steps जोड़ने से creative precision बढ़ती है।
ENGLISH: Creative prompts generate stories, scripts, campaigns, and imaginative outputs. Without constraints, the model tends to drift into clichés and generic patterns. Specifying style, length, perspective, and originality hooks improves creative precision.
HINGLISH: ImaginationRig™ Creativity भी rails मांगती है. “Style + length + POV + twist” दोगे तो output unique आएगा. Constraints नहीं तो AI default clichés पकड़ लेता है.
Day-to-day example: “Ruskin Bond style, 800 words, one twist.”
Anchor hook: “Creative = freedom + rails.”
Recall key: ImaginationRig = imagination with rules.
Instruction Stacking / निर्देश-स्तरीकरण StepStack™
HINDI: Instruction stacking में आप एक ही prompt में multiple tasks जोड़ देते हैं। इससे efficiency बढ़ती है, लेकिन AI steps skip कर सकता है अगर numbering/sequence clear नहीं है। इसलिए numbered steps, strict output format, और checklist confirmation जैसे controls लगाने से stacking reliable बनता है।
ENGLISH: Instruction stacking combines multiple tasks in one prompt. It improves efficiency but increases the risk of the model skipping steps unless tasks are numbered and the output format is enforced. Stacking works best with clear ordering and strict output rules.
HINGLISH: StepStack™ Multiple kaam ek prompt me karwa sakte ho, but AI “shortcut” ले सकता है. Steps number karo, output format lock करो, aur end me checklist मांगो.
Day-to-day example: “1) Summarize 2) Translate 3) Table” - order clear.
Anchor hook: “Stack करो, but steps lock करो.”
Recall key: StepStack = numbered steps or risk.
Comparison Prompts / तुलना प्रॉम्प्ट SideBySide™
HINDI: Comparison prompts options को common dimensions (risk, cost, tax, time, etc.) पर evaluate करवाते हैं। जोखिम तब है जब AI facts invent करे। “Unknown if not available” और source binding जोड़ने से misinformation कम होता है और comparison decision-ready बनता है।
ENGLISH: Comparison prompts evaluate options across common dimensions to support decisions. They are useful but risky when the model invents facts. Adding “Unknown if not available” and source binding protects against confident misinformation.
HINGLISH: SideBySide™ Comparison tab strong hota hai jab “same yardstick” fixed हो. AI ko बोलो: facts नहीं मिले तो “Unknown” लिखो. High-stakes में sources bind करो.
Day-to-day example: Phone compare: battery, camera, price - same columns.
Anchor hook: “Same scale, fair compare.”
Recall key: SideBySide = same columns for all.
System Prompts / सिस्टम प्रॉम्प्ट InvisibleConstitution™
HINDI: System prompts उच्च-प्राथमिकता निर्देश होते हैं जो पूरे session में AI के व्यवहार की सीमा तय करते हैं - tone, safety, refusal rules, escalation, policy adherence आदि। Agent systems में ये “संविधान” की तरह काम करते हैं। अच्छी system instructions से consistent behavior मिलता है और unsafe outputs घटते हैं।
ENGLISH: System prompts are hidden, top-priority instructions that shape the model’s behavior across a session. They define boundaries, tone, safety policies, refusal rules, and escalation behavior. In agent systems, system prompts act like a constitution.
HINGLISH: InvisibleConstitution™ System prompt = AI ka “rules book” jo sabse ऊपर रहता है. User kuch bhi bole, constitution boundaries maintain कराता है. यही safety + compliance का base है.
Day-to-day example: Company policy manual - employee ka behavior guide करता है.
Anchor hook: “Constitution ऊपर, बाकी नीचे.”
Recall key: InvisibleConstitution = top rules always.
Meta-Prompts / मेटा-प्रॉम्प्ट PromptSmith™
HINDI: Meta-prompts AI को prompts बनाने/सुधारने/जांचने के लिए निर्देश देते हैं। ये user goal को structured prompt में बदलते हैं और critique loops जोड़कर clarity, constraints, bias reduction करते हैं। इससे non-technical teams भी बेहतर prompting कर पाती हैं और reusable templates बनते हैं।
ENGLISH: Meta-prompts instruct the model to generate, optimize, or evaluate prompts. They translate a user goal into a high-quality prompt, often including critique loops to improve clarity, reduce bias, and add constraints. Meta-prompts enable non-technical teams to prompt well.
HINGLISH: PromptSmith™ Meta-prompt = “prompt banane wala prompt.” Tum goal दो, AI खुद best prompt draft करता है, फिर खुद critique करके improve करता है. Team ke लिए prompt factory बन जाता है.
Day-to-day example: Resume ke लिए template generator.
Anchor hook: “Prompt ka लोहार = PromptSmith.”
Recall key: PromptSmith = prompt that writes prompts.
Moodboard Prompting / मूडबोर्ड प्रॉम्प्टिंग (भाव-मैप से दिशा) MoodMap™ (Moodboard: vibe ka map)
HINDI: Moodboard Prompting में आप keywords और constraints से desired vibe/aesthetic define करते हैं—जैसे calm, premium, minimal, energetic। यह creative outputs (copy, titles, concepts) को सही दिशा देता है। अच्छा moodboard prompt include + avoid दोनों बताता है ताकि tone off न हो।
ENGLISH: Moodboard prompting describes the desired aesthetic and emotional palette using keywords, references, and constraints (e.g., calm, premium, minimal). It guides creative outputs like copy, titles, and concepts. Best practice is to specify what to include and what to avoid.
HINGLISH: MoodMap™ (Moodboard: vibe ka map) Moodboard = vibe ka map: “yeh feel chahiye, yeh nahi.” AI ko clear emotional palette doge toh output consistent लगेगा.
Day-to-day example: Shaadi card: classy minimal vs loud flashy — mood तय करो.
Anchor hook: “Vibe define, output align.
Recall key: MoodMap = feel words + avoid list.
Prompt Pipelines / प्रॉम्प्ट पाइपलाइन AssemblyLine™
HINDI: Prompt pipeline engineered sequence है जो repeatable outcomes देती है। यह modules अलग करके checkpoints डालती है, जिससे reliability, auditability, और scale बढ़ता है। Pipeline बनाते समय routing, evaluator gates, और stage-wise metrics जोड़ना best practice है।
ENGLISH: A prompt pipeline is an engineered sequence of prompt components designed for repeatable outcomes. Pipelines improve reliability, auditability, and scale in real systems by separating tasks into stable modules and adding checkpoints between stages.
HINGLISH: AssemblyLine™ Pipeline = factory workflow. हर stage का काम fixed, output next stage को. Checkpoints रखो ताकि गलत output आगे ना जाए.
Day-to-day example: Factory line: quality check ke bina product ship नहीं होता.
Anchor hook: “AI bhi assembly line चाहता है.”
Recall key: AssemblyLine = repeatable stages + checks.
Prompt Architecture / प्रॉम्प्ट आर्किटेक्चर PromptBlueprint™
HINDI: Prompt architecture system-level design है जहाँ multiple prompts, roles, checks, and flows मिलकर reliable outputs produce करते हैं। यह prompts को ad-hoc text नहीं, engineered components मानता है। अच्छी architecture edge cases, governance, और auditing needs पहले से anticipate करती है।
ENGLISH: Prompt architecture is the system-level design of multiple prompts, roles, checks, and flows that work together to produce reliable outputs. It treats prompts as engineered components rather than ad-hoc text. Good architecture anticipates edge cases and governance needs.
HINGLISH: PromptBlueprint™ Architecture मतलब prompts ka “system design.” Kaun सा prompt कब चलेगा, checks कहाँ होंगे, कौन approve करेगा - सब पहले से. Isse production-grade reliability आती है.
Day-to-day example: Building blueprint: plumbing, wiring, सब plan में.
Anchor hook: “Prompt bhi building है - blueprint चाहिए.”
Recall key: PromptBlueprint = system design of prompts.
Hierarchical Prompting / पदानुक्रमित प्रॉम्प्टिंग ManagerWorker™
HINDI: Hierarchical prompting में manager prompt planning करता है और worker prompts execution करते हैं। Planning और execution अलग होने से missed steps कम होते हैं और control बढ़ता है। यह human team structure जैसा है - एक coordinator, कई executors।
ENGLISH: Hierarchical prompting uses a manager prompt to plan and coordinate multiple worker prompts. It reduces missed steps in complex tasks by separating planning from execution. It mirrors how human teams operate: one coordinator, many executors.
HINGLISH: ManagerWorker™ Ek manager “plan” बनाता है, workers “do” करते हैं. Isse chaos कम और ownership clear. Worker outputs ka format lock करना जरूरी है.
Day-to-day example: Team lead task बाँटता है, members deliver करते हैं.
Anchor hook: “Manager सोचता, worker करता.”
Recall key: ManagerWorker = plan then execute.
Multi-Agent Prompting / बहु-एजेंट प्रॉम्प्टिंग AgentSwarm™
HINDI: Multi-agent prompting में कई specialized agents (searcher, analyzer, writer, reviewer) मिलकर output बनाते हैं। Specialization depth और speed बढ़ाता है, लेकिन orchestration, checks, और ownership clear न हो तो reliability गिर सकती है। इसलिए reviewer/evaluator agent और escalation rules जोड़ना best है।
ENGLISH: Multi-agent prompting uses multiple specialized agents (searcher, analyzer, writer, reviewer) collaborating to produce higher-quality outcomes. Specialization improves depth and speed, but requires orchestration, checks, and clear ownership to remain reliable.
HINGLISH: AgentSwarm™ Agents ka swarm = specialist team. Ek research kare, ek लिखे, ek review करे. But rules नहीं होंगे तो “conflicting outputs” आएंगे.
Day-to-day example: Newsroom: reporter → editor → fact-checker.
Anchor hook: “Many brains, one system.”
Recall key: AgentSwarm = roles divide, then merge.
Memory-Augmented Prompting / स्मृति-वर्धित प्रॉम्प्टिंग LongRecall™
HINDI: Memory-augmented prompting context window की सीमा को external memory stores (database, vector store, past chats) से relevant info खींचकर extend करता है। इससे continuity, personalization, और repetition कम होता है। लेकिन privacy, accuracy, और governance जरूरी हैं - क्या store करना है, क्या retrieve करना है, और क्या दिखाना safe है।
ENGLISH: Memory-augmented prompting extends limited context windows by pulling relevant information from external memory stores (databases, vector stores, prior chats). It improves continuity, personalization, and reduces repetition. Memory must be governed for privacy and accuracy.
HINGLISH: LongRecall™ AI ko “external notebook” दे दो - woh जरूरी points retrieve करके काम करेगा. But memory policy strict रखो, वरना privacy risk.
Day-to-day example: Customer support में old ticket history देखकर reply.
Anchor hook: “Short memory + external diary = LongRecall.”
Recall key: LongRecall = external memory retrieval.
A/B Prompt Testing / ए/बी प्रॉम्प्ट टेस्टिंग (दो वर्ज़न की तुलना) AB-Ring™ (A/B: do prompts ka मुकाबला)
HINDI: A/B Prompt Testing में आप एक ही input set पर दो prompt versions चलाकर compare करते हैं कि कौन बेहतर perform करता है—metrics जैसे accuracy, tone match, format compliance, or user satisfaction के आधार पर। इससे “मुझे ये अच्छा लगा” वाली बहस कम होती है और data-driven improvement होता है।
ENGLISH: A/B prompt testing compares two prompt versions on the same inputs to measure which performs better on defined metrics (accuracy, tone, format compliance). It prevents subjective debates and supports data-driven prompt improvement.
HINGLISH: AB-Ring™ (A/B: do prompts ka मुकाबला) A/B Prompt Testing में आप एक ही input set पर दो prompt versions चलाकर compare करते हैं कि कौन बेहतर perform करता है—metrics जैसे accuracy, tone match, format compliance, or user satisfaction के आधार पर। इससे “मुझे ये अच्छा लगा” वाली बहस कम होती है और data-driven improvement होता है।
Prompt-Orchestration with RAG / RAG के साथ प्रॉम्प्ट ऑर्केस्ट्रेशन EvidenceFlow™
HINDI: Orchestrated RAG retrieval को structured templates और quality gates (evaluator/reviewer) के साथ जोड़ता है। यह RAG को single prompt से उठाकर controlled system बनाता है। इससे trust, consistency और scalability बढ़ती है, और monitoring metrics (accuracy, hallucination rate) track हो पाते हैं।
ENGLISH: Orchestrated RAG combines retrieval with structured prompt templates and quality gates such as evaluator or reviewer agents. It turns RAG into a controlled system rather than a single prompt. This improves trust and scalability in enterprise usage.
HINGLISH: EvidenceFlow™ RAG + orchestration = evidence pipeline. Retrieve करो, generate करो, evaluator से check कराओ, फिर final. यही enterprise trust बनाता है.
Day-to-day example: Draft → manager review → final mail.
Anchor hook: “Evidence with checkpoints.”
Recall key: EvidenceFlow = RAG + checks + routing.
PromptOps – Managing Prompts Like Code / PromptOps (प्रॉम्प्ट को कोड की तरह संभालना) PromptOpsCore™
HINDI: PromptOps prompts को versioning, testing, monitoring और governance के साथ “software asset” की तरह manage करने की discipline है। इससे prompt sprawl कम होता है, production risk घटता है, और audit-ready workflows बनते हैं। PromptOps में owners, releases, golden sets, और CI-style testing जैसी practices आती हैं।
ENGLISH: PromptOps is the operational discipline of versioning, testing, monitoring, and governing prompts at scale. It treats prompts like software assets with owners, releases, and audits. PromptOps prevents inconsistent prompts across teams and reduces production risk.
HINGLISH: PromptOpsCore™ Prompts ko “casual text” मत समझो - ये production code जैसे हैं. Version control + tests + monitoring लगाओ. तभी system predictable रहेगा.
Day-to-day example: App update बिना testing के release नहीं करते. Prompt भी नहीं.
Anchor hook: “Prompt = code asset.”
Recall key: PromptOps = version + test + monitor.
Prompt Versioning / प्रॉम्प्ट संस्करण-नियंत्रण PromptVersion™
HINDI: Prompt versioning में prompts को version numbers देकर changes, owners, और performance track किया जाता है। इससे controlled rollout, rollback और experimentation possible होता है। High-volume या regulated systems में versioning जरूरी है ताकि कौन-सा prompt किस output के लिए responsible है, trace हो सके।
ENGLISH: Prompt versioning assigns version numbers to prompts and tracks changes, owners, and performance. It enables controlled rollout, rollback, and learning from experiments. Versioning is essential when prompts affect customers, compliance, or high-volume workflows.
HINGLISH: PromptVersion™ Prompt ka v1, v1.1, v2 - exactly software jaisa. Agar नई version से output बिगड़ गया, तुरंत rollback.
Day-to-day example: WhatsApp update buggy हो तो पुराने version पर जाना.
Anchor hook: “Change control = trust control.”
Recall key: PromptVersion = track + rollback.
Prompt Lifecycle / प्रॉम्प्ट जीवनचक्र PromptLifeCycle™
HINDI: Prompt lifecycle stages हैं: design, evaluate, deploy, monitor, iterate, retire. Lifecycle governance के बिना prompts एक-time hack बनकर drift करते रहते हैं। Lifecycle से ownership और review cadence तय होता है, जिससे prompts production-grade “process” बनते हैं, accident नहीं।
ENGLISH: Prompt lifecycle defines stages: design, evaluate, deploy, monitor, iterate, retire. Without lifecycle governance, prompts remain one-time hacks and drift silently over time. Lifecycle makes prompt quality a repeatable process, not a one-off event.
HINGLISH: PromptLifeCycle™ Prompt ko birth se retirement तक manage करो. Monitor नहीं करोगे तो silent drift होगा और एक दिन system fail.
Day-to-day example: Policy documents भी periodic review मांगते हैं.
Anchor hook: “Prompts age too.”
Recall key: LifeCycle = design→deploy→monitor→retire.
Prompt Drift / प्रॉम्प्ट ड्रिफ्ट DriftShock™
HINDI: Prompt drift तब होता है जब छोटे wording changes output में बड़ा behavior change कर देते हैं। इससे system fragile और unpredictable बनता है। Drift risk तब ज्यादा होता है जब multiple लोग prompts edit करते हैं लेकिन regression tests नहीं चलते। Golden set testing drift को पकड़ने का best तरीका है।
ENGLISH: Prompt drift happens when small wording changes cause large output shifts. It makes systems fragile, unpredictable, and hard to debug. Drift risk increases when multiple people edit prompts without testing.
HINGLISH: DriftShock™ “Brief” को “Explain” कर दिया और output double हो गया - यही drift shock है. Small edit, big behavior. इसलिए हर change के बाद golden set test जरूरी.
Day-to-day example: Recipe me 1 चम्मच की जगह 1 कप salt.
Anchor hook: “Small edit, big blast.”
Recall key: DriftShock = tiny change, huge shift.
Shadow Prompts / शैडो प्रॉम्प्ट PromptShadowing™
HINDI: Shadow prompts वे unofficial prompts हैं जो approved prompt library के बाहर बनते/चलते हैं। ये duplication, inconsistent outputs, और governance gaps पैदा करते हैं, खासकर regulated domains में। Shadow prompt sprawl एक hidden risk है क्योंकि audits और ownership टूट जाते हैं।
ENGLISH: Shadow prompts are unofficial prompts created outside the approved prompt library. They cause duplication, inconsistent outputs, and governance gaps - especially in regulated or customer-facing systems. Shadow prompts are a hidden source of prompt chaos.
HINGLISH: PromptShadowing™ Team A ka prompt अलग, Team B ka अलग - output mismatch और blame game. Central library नहीं होगी तो shadow prompts फैलेंगे.
Day-to-day example: हर department अपनी “Excel sheet” चला रहा है - data mismatch.
Anchor hook: “Hidden prompts, hidden chaos.”
Recall key: PromptShadowing = unofficial prompt sprawl.
Prompts as System Components / प्रॉम्प्ट एक सिस्टम-कंपोनेंट PromptAsCode™
HINDI: Production में prompts software components की तरह behave करते हैं: interfaces, constraints, owners, versions, tests। Prompts को casual text मानने से reliability और auditing टूट जाती है। Best practice है input/output contracts define करना, repos में store करना, tests + approvals attach करना।
ENGLISH: In production, prompts behave like software components: they have interfaces, constraints, owners, versions, and tests. Treating prompts as casual text breaks reliability and auditing. Prompt components should be designed, documented, and governed like code.
HINGLISH: PromptAsCode™ Prompt ko “asset” मानो. Input variable, output schema, version, tests - सब define. तभी system scalable होगा.
Day-to-day example: API का contract होता है; prompt का भी होना चाहिए.
Anchor hook: “Prompt is a component, not a message.”
Recall key: PromptAsCode = contracts + versions + tests.
Compliance & Ethics
Reliability / विश्वसनीयता TrustGrade™
HINDI: Reliability का मतलब है prompt अपने intended use-case के लिए correct, consistent और safe outputs दे। High-stakes domains में unreliable AI “confidently wrong” होकर नुकसान कर सकता है। इसलिए reliability bonus नहीं, design requirement है - constraints, checks, monitoring के साथ build करनी होती है।
ENGLISH: Reliability means a prompt produces correct, consistent, and safe outputs for its intended use-case. In high-stakes domains, unreliable AI is worse than no AI because errors can be confidently wrong. Reliability is a design requirement, not a bonus feature.
HINGLISH: TrustGrade™ AI output ka भरोसा तभी जब बार-बार same input पर stable, safe, correct result दे. BFSI/health/legal में “confident गलत” सबसे dangerous है.
Day-to-day example: Calculator अगर 2+2 कभी 4, कभी 5 दे - useless.
Anchor hook: “Trust = repeatable truth.”
Recall key: TrustGrade = correct + consistent + safe.
4 Enemies of Reliable Prompts / विश्वसनीयता के 4 शत्रु RiskQuadrant™
HINDI: चार शत्रु हैं: hallucinations (कल्पित तथ्य), bias (पक्षपात), overgeneralization (ज़रूरत से ज्यादा सामान्य निष्कर्ष), और fragility (छोटी change पर बड़ा break)। Prompt engineering का real काम इन failure modes को guardrails, examples, evaluation और monitoring से कम करना है। इन्हें manage न किया जाए तो output trust collapse हो जाता है।
ENGLISH: The four enemies are hallucinations, bias, overgeneralization, and fragility. Prompt engineering in practice is reducing these failure modes through guardrails, examples, evaluation, and monitoring. If these enemies are unmanaged, output trust collapses.
HINGLISH: RiskQuadrant™ Reliable prompt ke 4 dushman: hallucination, bias, overgeneralize, fragility. Inko map karo, फिर tests बनाओ जो हर enemy को hit करें.
Day-to-day example: Exam me 4 types ki mistakes hoti हैं - same concept.
Anchor hook: “Enemy पहचानो, system मजबूत करो.”
Recall key: RiskQuadrant = 4 enemies checklist.
Guardrails in Prompt Design / प्रॉम्प्ट गार्डरेल्स RailSystem™
HINDI: Guardrails वे boundaries हैं जो output को safe और usable बनाती हैं - length limits, format rules, domain scope, ethics constraints। ये drift कम करती हैं और non-compliant outputs रोकती हैं। Guardrails खासकर तब जरूरी हैं जब AI decisions, customers या compliance को impact करता है।
ENGLISH: Guardrails are boundaries that keep outputs safe and usable: length limits, format rules, domain scope, and ethics constraints. Guardrails reduce drift and prevent unsafe or non-compliant outputs. They are essential when AI affects decisions or customers.
HINGLISH: RailSystem™ Guardrails = track ke rails. Train ko direction milती है, derail नहीं होती. Format, scope, safety rules clearly लिखो, और end me key rules repeat करो.
Day-to-day example: Road pe divider - accident कम.
Anchor hook: “Rails = safe output.”
Recall key: RailSystem = boundaries prevent drift.
Reliability Triangle / विश्वसनीयता त्रिकोण C-C-C Triangle™
HINDI: Reliability तीन sides पर टिकी है: Clarity (क्या करना है), Constraints (क्या नहीं करना), Checks (कैसे verify करना)। इनमें से एक भी कमजोर हो तो reliability गिरती है। यह triangle prompt audit करने का practical तरीका है - देखो कौन-सा side सबसे कमजोर है।
ENGLISH: Reliability depends on three sides: Clarity (what to do), Constraints (what not to do), and Checks (how to verify). If any side is missing, reliability collapses. This triangle is a practical way to audit prompt readiness.
HINGLISH: C-C-C Triangle™ Clarity + Constraints + Checks - teenon जरूरी. Sirf clarity होगी तो AI guess करेगा; checks नहीं होंगे तो गलत पकड़ा नहीं जाएगा.
Day-to-day example: Exam: syllabus + rules + answer-key checking.
Anchor hook: “3C missing = trust missing.”
Recall key: CCC = Clarity-Constraints-Checks.
SAFE Prompting Model / SAFE प्रॉम्प्टिंग मॉडल SAFE-Lock™
HINDI: SAFE = Source Binding, Ask for Balance, Format Rules, Evaluation. यह trust-critical prompting के लिए formula है: sources से bind करो, balanced view मांगो, output format lock करो, और self-check/evaluation step जोड़ो। SAFE hallucination, bias और messy outputs को reduce करता है।
ENGLISH: SAFE is a prompt reliability formula: Source Binding, Ask for Balance, Format Rules, Evaluation. It improves grounding, reduces bias, enforces structure, and adds verification. SAFE is designed for trust-critical prompting in real workflows.
HINGLISH: SAFE-Lock™ SAFE matlab prompt ko lock karna: sources fix, balance मांगो, format fixed, evaluation mandatory. Ye BFSI/Legal/Policy me सबसे useful है.
Day-to-day example: “Sirf policy text use करो, pros/cons दो, table में, aur end में self-check.”
Anchor hook: “SAFE = trust lock.”
Recall key: SAFE = Sources + Balance + Format + Evaluate.
Reliability Testing Workflow / विश्वसनीयता टेस्टिंग वर्कफ़्लो TestLoop™
HINDI: Reliability testing एक repeatable workflow है: prototype, stress test, audit, refine, document। इससे prompting intuition से निकलकर measurable quality बनता है। Testing ही prompts को demo-grade से production-grade बनाती है।
ENGLISH: Reliability testing is a repeatable workflow: prototype, stress test, audit, refine, document. It moves prompting from intuition to measurable quality. Testing is how prompts become production-grade rather than demo-grade.
HINGLISH: TestLoop™ Test नहीं तो trust नहीं. Diverse inputs चलाओ, failures नोट करो, constraints improve करो, फिर दोबारा test. यही loop prompt maturity बनाता है.
Day-to-day example: New phone launch से पहले QA testing.
Anchor hook: “Test → fix → repeat.”
Recall key: TestLoop = measure, then improve.
Golden Sets / गोल्डन सेट्स GoldStandardSet™
HINDI: Golden sets curated inputs हैं जिनके expected outputs पहले से verified होते हैं। ये evaluation baseline बनाते हैं और prompt changes को measurable करते हैं। Edge cases और real failure samples जोड़कर golden set को evolve करना best practice है।
ENGLISH: Golden sets are curated inputs with expected outputs used to measure correctness and consistency. They create a baseline for evaluation and make prompt changes measurable. Golden sets are essential for stable iteration and governance.
HINGLISH: GoldStandardSet™ Golden set = “official answer-key dataset.” Prompt update के बाद इसी पर regression test चलाओ. तभी पता चलेगा improvement हुआ या break.
Day-to-day example: Mock test की answer key.
Anchor hook: “If you can’t measure, you can’t trust.”
Recall key: GoldStandardSet = test inputs with expected outputs.
Adversarial Testing / प्रतिकूल (Adversarial) टेस्टिंग BreakToBuild™
HINDI: Adversarial testing prompts को tricky/hostile inputs से stress करता है ताकि vulnerabilities सामने आएँ। इसका उद्देश्य misuse enable करना नहीं, defense मजबूत करना है। यह jailbreak success और unsafe output risk घटाने के लिए जरूरी practice है।
ENGLISH: Adversarial testing stresses prompts with tricky, misleading, or hostile inputs to reveal vulnerabilities. It is defensive engineering meant to harden systems, not enable misuse. Adversarial testing reduces jailbreak success and unsafe output risk.
HINGLISH: BreakToBuild™ System ko “attack-like” prompts se test करो ताकि weak points fix हों. Safe deployment के लिए ये जरूरी है.
Day-to-day example: Fire drill - आग लगने से पहले practice.
Anchor hook: “Break it safely, build it stronger.”
Recall key: BreakToBuild = stress test for defense.
Tool Misuse Risk / टूल मिसयूज़ जोखिम (गलत काम के लिए टूल चलवाना) ToolTrap™ (tools se galat kaam karwana)
HINDI: Tool Misuse Risk तब होता है जब user AI system को tools (web, files, actions) से harmful/illegal/unauthorized काम करवाने की कोशिश करे—जैसे credential harvesting, data scraping, permissions bypass। रोकथाम के लिए strict permissions, logging, और refusal policies जरूरी हैं।
ENGLISH: Tool misuse risk is when users try to get an AI system to use tools (web, files, actions) for harmful, illegal, or unauthorized outcomes. It includes credential harvesting, data scraping, and bypassing permissions. Mitigation requires strict permissions, logging, and refusal policies.
HINGLISH: ToolTrap™ (tools se galat kaam karwana) Tool Misuse Risk तब होता है जब user AI system को tools (web, files, actions) से harmful/illegal/unauthorized काम करवाने की कोशिश करे—जैसे credential harvesting, data scraping, permissions bypass। रोकथाम के लिए strict permissions, logging, और refusal policies जरूरी हैं।
Audit Trails / ऑडिट ट्रेल्स TraceProof™
HINDI: Audit trails prompts, inputs, outputs और versions को log करके traceability देते हैं। ये compliance, debugging, incident response, और accountability के लिए foundation हैं। Regulated systems में audit trail के बिना trust और governance कमजोर हो जाती है।
ENGLISH: Audit trails log prompts, inputs, outputs, and versions so decisions remain traceable. They support compliance, debugging, incident response, and accountability. In regulated systems, audit trails are a foundation of trust and governance.
HINGLISH: TraceProof™ “Kaun सा prompt, kis input pe, kya output” - सब record. Jab problem हो, root cause तुरंत निकलता है.
Day-to-day example: Bank statement - हर transaction traceable.
Anchor hook: “No logs, no trust.”
Recall key: TraceProof = traceable history.
Dark Side of Prompt Engineering Techniques / प्रॉम्प्टिंग का दुरुपयोग पक्ष FUTURE6™ (6-step ethics frame)
HINDI: FUTURE Model एक practical AI-ethics framework है जो real work में AI का जिम्मेदार उपयोग करवाता है। यह harm कम करता है, trust बढ़ाता है, और outputs को human benefit के साथ aligned रखता है। FUTURE का मतलब है—Fairness, Use-Case Fit, Transparency, User Safety, Responsible Data, और Explainability।
ENGLISH: The FUTURE Model is a practical AI-ethics framework that guides how to use AI responsibly across real work. It helps teams reduce harm, improve trust, and keep outputs aligned with human benefit. FUTURE stands for Fairness, Use-Case Fit, Transparency, User Safety, Responsible Data, and Explainability.
HINGLISH: FUTURE6™ = ethics ka quick checklist. Jab bhi AI use karo, 6 सवाल पूछो: Fair hai? Use-case fit hai? Transparent hai? User safe hai? Data responsibly handle ho raha? Explainable hai?\n\nDay-to-day example: Online delivery app choose करते waqt—rating, safety, refund policy, data privacy—sab check.\nAnchor hook: “AI use karne se pehle FUTURE check.”\nRecall key: F-U-T-U-R-E = 6 ethics switches ON.
F.U.T.U.R.E. Model / FUTURE मॉडल (AI ethics का 6-पार्ट फ्रेमवर्क) EthicsByDesign™
HINDI: Ethical guardrails transparency, source binding, bias testing, error recovery, और access control को prompt systems में embed करते हैं। Ethics अलग layer नहीं - prompts, pipelines और ops में built-in होनी चाहिए। Ethical design harm कम करता है और trust बढ़ाता है, खासकर customer-facing workflows में।
ENGLISH: Ethical guardrails embed transparency, source binding, bias testing, error recovery, and access control into prompt systems. Ethics is not a separate layer; it must be built into prompts, pipelines, and operations. Ethical design reduces harm and increases trust in AI outputs.
HINGLISH: EthicsByDesign™ Ethics ko “afterthought” मत बनाओ - prompt design ke अंदर ही lock करो. Source binding + uncertainty + refusal rules - ये सब system का हिस्सा हो.
Day-to-day example: Car में seatbelt built-in होता है, optional नहीं.
Anchor hook: “Ethics is engineering.”
Recall key: EthicsByDesign = ethics built into prompts.
Psychological Risks / मनोवैज्ञानिक जोखिम HumanTrapMap™
HINDI: Psychological risks में authority bias, dependency loops, और “AI objective है” वाली illusion शामिल है। ये risks human side पर होते हैं, इसलिए prompt design में humility, uncertainty disclosure, और escalation rules जरूरी हैं। High-stakes में human review mandatory बनाना चाहिए ताकि over-trust से harm न हो।
ENGLISH: Psychological risks include authority bias, dependency loops, and the illusion of objectivity caused by confident AI tone. These risks occur on the human side, so prompt design must include humility, uncertainty disclosure, and escalation when needed. Trust must be engineered, not assumed.
HINGLISH: HumanTrapMap™ AI confident बोलता है, और हम उसे “expert” मान लेते हैं - यही trap है. Prompt में uncertainty + limits + “human review” जोड़ो. Trust engineer करना पड़ता है.
Day-to-day example: Google result top होने से सच्चा नहीं होता.
Anchor hook: “Confidence ≠ correctness.”
Recall key: HumanTrapMap = over-trust risks.
E.T.H.I.C Model / E.T.H.I.C मॉडल ETHIC-Lens™
HINDI: ETHIC = Explainability, Transparency, Harm Prevention, Integrity, Compliance. यह values को testable checkpoints में बदलता है। Teams इसे release checklist की तरह use करके bias, harm और policy violations कम कर सकती हैं। Real-world pressure में भी safe behavior बनाए रखने में मदद करता है।
ENGLISH: ETHIC operationalizes ethical prompting: Explainability, Transparency, Harm Prevention, Integrity, and Compliance. It converts values into checkpoints that can be tested and audited. ETHIC helps teams design prompts that remain safe under real-world pressure.
HINGLISH: ETHIC-Lens™ ETHIC = ethics ko “checklist” bana do. Explain karo, disclose karo, harm रोकों, integrity रखो, compliance follow करो.
Day-to-day example: Flight checklist - safety repeatable बनती है.
Anchor hook: “Ethics = checklist, not vibes.”
Recall key: ETHIC = explain + transparent + safe + honest + comply.
Red-Team (Responsible Use) + Attack Surface Catalogue / रेड-टीम + अटैक सरफेस कैटलॉग RedTeamAtlas™
HINDI: Red-teaming isolated environments में responsible तरीके से AI को test करता है ताकि weaknesses fix की जा सकें। Core vectors में prompt injection, data leakage, jailbreaks, poisoning, social engineering, laundering chains शामिल हैं। इसे recurring regression suite की तरह चलाना safer deployment के लिए जरूरी है।
ENGLISH: Red-teaming tests AI systems to reveal weaknesses so they can be fixed, using isolated environments and responsible disclosure. Core vectors include prompt injection, data leakage, jailbreaks, poisoning, social engineering, and laundering chains. Red-teaming is a defense practice for safer deployment.
HINGLISH: RedTeamAtlas™ Red-team = controlled attack simulation. Attack surface map बना लो, और हर vector पर test suite चलाओ. Goal “break to fix” है, misuse नहीं.
Day-to-day example: Cybersecurity penetration testing.
Anchor hook: “Test like attacker, build like defender.”
Recall key: RedTeamAtlas = attack map + defense tests.
FutureAI
Prompts in Production / प्रोडक्शन में प्रॉम्प्ट ProductionGrade™
HINDI: Production prompts को consistent, auditable, safe और scalable होना चाहिए। इसके लिए templates, governance, testing, monitoring, और ownership जरूरी है - सिर्फ clever one-liners नहीं। Production prompting experimentation नहीं, engineering है।
ENGLISH: Production prompts must be consistent, auditable, safe, and scalable. This requires templates, governance, testing, monitoring, and ownership - not clever one-liners. Production prompting is engineering, not experimentation.
HINGLISH: ProductionGrade™ Production me prompt = product behavior. Template + logs + version + tests के बिना risk. “Cool prompt” नहीं, “stable prompt” चाहिए.
Day-to-day example: ATM software मज़ाक नहीं कर सकता - prompt भी नहीं.
Anchor hook: “Production = engineered.”
Recall key: ProductionGrade = stable + auditable + safe.
P-R-O-D Model / P-R-O-D मॉडल PROD-Stack™
HINDI: PROD = Pipeline, RAG, Ops, Documentation. यह deployment checklist है ताकि prompts modular हों, trusted sources से grounded हों, operationally governed हों, और properly documented हों। PROD prompt experiment को shippable system में बदलता है।
ENGLISH: PROD is a deployment model: Pipeline, RAG, Ops, Documentation. It ensures prompts are modular, grounded in trusted sources, operationally governed, and properly recorded. PROD turns a prompt experiment into a shippable system.
HINGLISH: PROD-Stack™ PROD मतलब ship करने से पहले 4 चीज़ें: pipeline, RAG grounding, ops governance, docs. Inme se एक missing तो production risk.
Day-to-day example: Restaurant: process + quality + operations + menu docs.
Anchor hook: “No PROD, no ship.”
Recall key: PROD = Pipeline-RAG-Ops-Docs.
C.A.R.E Model for PromptOps / PromptOps के लिए CARE मॉडल CARE-Governance™
HINDI: CARE = Centralize prompts, Audit outputs, Refine continuously, Educate teams. यह prompt duplication और governance failures को रोकता है। Central registry + training + audits से prompt chaos कम होता है और organizational prompting mature होता है।
ENGLISH: CARE operationalizes PromptOps: Centralize prompts, Audit outputs, Refine continuously, Educate teams. It reduces prompt duplication and governance failures by creating a shared system for improvement and control. CARE is how organizations prevent prompt chaos.
HINGLISH: CARE-Governance™ CARE मतलब prompt culture बनाओ: central library, audits, continuous improvement, team training. Tabhi org-level consistency आएगी.
Day-to-day example: Company SOPs - सब एक जगह.
Anchor hook: “Care for prompts like assets.”
Recall key: CARE = centralize-audit-refine-educate.
A-R-C-H Model / A-R-C-H मॉडल ARCH-Orchestrator™
HINDI: ARCH = Agents, Relationships, Checks, Hierarchy. यह multi-agent systems के लिए structure देता है: roles कौन, handoffs कैसे, verification gates कहाँ, coordination कैसे। ARCH failure propagation कम करता है और complex workflows को manageable बनाता है।
ENGLISH: ARCH guides advanced prompt architectures: Agents, Relationships, Checks, and Hierarchy. It ensures multi-agent systems have clear roles, defined handoffs, verification gates, and coordination structure. ARCH reduces failure propagation in complex AI workflows.
HINGLISH: ARCH-Orchestrator™ ARCH se agent network clean banta है: agents, relationships, checks, hierarchy. बिना checks के errors chain में फैलते हैं.
Day-to-day example: Office workflow: maker-checker-approver.
Anchor hook: “Agents need org chart.”
Recall key: ARCH = agents + handoffs + checks + hierarchy.
Multi-Agent Societies / बहु-एजेंट समाज AgentSociety™
HINDI: Multi-agent societies specialized agents का network है जो human teams की तरह collaborate करता है। भविष्य में humans micro-prompts लिखने की बजाय goals और evaluation manage करेंगे। इससे skill shift होता है: prompt writing से orchestration + governance पर।
ENGLISH: Multi-agent societies are networks of specialized agents collaborating like human teams. Humans increasingly manage goals and evaluation rather than writing every micro-prompt. This shifts the skill from prompt writing to orchestration and governance.
HINGLISH: AgentSociety™ Future me AI agents ek team की तरह काम करेंगे. Human का काम: goal set करना, quality evaluate करना, governance रखना.
Day-to-day example: Film crew: director sets vision, team executes.
Anchor hook: “From prompt writer to AI manager.”
Recall key: AgentSociety = many agents, one goal.
Convergence of Prompting + Programming / प्रॉम्प्टिंग + प्रोग्रामिंग का सम्मिलन NaturalLanguageDev™
HINDI: Prompts और code की boundary shrink हो रही है: prompts specifications बन रहे हैं, specifications APIs बन रहे हैं, workflows language + software का hybrid बन रहे हैं। Prompt engineering natural language programming में evolve हो रही है जहाँ humans intent बोलते हैं और system उसे execution में compile करता है।
ENGLISH: The boundary between prompts and code is shrinking: prompts become specifications, specifications become APIs, and workflows become hybrids of language + software. Prompt engineering evolves into natural language programming where humans express intent and systems compile it into execution.
HINGLISH: NaturalLanguageDev™ “English me instructions” धीरे-धीरे code जैसा काम करेंगे. Prompt = spec, spec = workflow. Skill बन रही है: intent साफ बोलो, system execute कराए.
Day-to-day example: “Build report pipeline” और tool chain auto-run.
Anchor hook: “Words become workflows.”
Recall key: NaturalLanguageDev = speak intent, system builds.
Beyond the Prompt Era / प्रॉम्प्ट युग के बाद PostPromptShift™
HINDI: Prompt engineering एक bridge skill है - आज जरूरी, पर धीरे-धीरे embedded और invisible हो जाएगी जब systems goal-spec, multimodal inputs, और autonomous agents की तरफ बढ़ेंगे। Prompting खत्म नहीं होगा; वह infrastructure बनकर products के अंदर छुप जाएगा। इसलिए long-term investment governance, evaluation, और workflow design में भी होना चाहिए।
ENGLISH: Prompt engineering is a bridge skill: essential now but increasingly embedded and invisible as systems move toward goal-spec, multimodal inputs, and autonomous agents. Prompting does not disappear; it becomes infrastructure inside products and workflows.
HINGLISH: PostPromptShift™ Future me users prompt type नहीं करेंगे - system goal समझकर behind-the-scenes prompts चलाएगा. Prompting invisible हो जाएगी, but governance बहुत visible होगी.
Day-to-day example: GPS me tum route नहीं लिखते, बस destination.
Anchor hook: “Prompt becomes plumbing.”
Recall key: PostPromptShift = prompting becomes infrastructure.
Trajectory of Prompting / प्रॉम्प्टिंग की यात्रा PromptTimeline™
HINDI: Prompting phases: hack phase → engineering phase → integration phase → post-prompt phase. हर phase में value shift होता है: individual tricks से organizational infrastructure, governance, और embedded workflows तक। Strategy teams इसे capability roadmapping के lens की तरह use कर सकती हैं।
ENGLISH: Prompting evolves through phases: hack phase, engineering phase, integration phase, and post-prompt phase. Each phase shifts value from individual clever prompts to organizational infrastructure, governance, and embedded workflows.
HINGLISH: PromptTimeline™ Start me hacks, फिर engineering, फिर integration, फिर invisible infrastructure. Org ko पता होना चाहिए वो किस phase में है ताकि next upgrade plan हो.
Day-to-day example: Startup growth: jugaad → process → scale → automation.
Anchor hook: “Tricks to systems.”
Recall key: PromptTimeline = phases of maturity.
Three Possible Futures / तीन संभावित भविष्य FutureFork™
HINDI: AI का भविष्य तीन दिशाओं में जा सकता है: optimistic (co-agency), neutral (invisible infrastructure), या dark (manipulative PsyOps)। कौन-सा path dominate करेगा यह governance, transparency, और ethical design choices पर निर्भर है। यह prediction नहीं, design responsibility है।
ENGLISH: AI can evolve into an optimistic future (co-agency), a neutral future (invisible infrastructure), or a dark future (manipulative PsyOps). Which path dominates depends on today’s governance, transparency, and ethical design choices. This is a strategic design responsibility, not a prediction game.
HINGLISH: FutureFork™ AI ka future ek fork hai: co-agency (help), invisible infra (normal), ya manipulative PsyOps (harm). Governance + transparency decide करेगी.
Day-to-day example: Knife: kitchen tool भी, weapon भी. Use + rules matter.
Anchor hook: “Future is designed, not guessed.”
Recall key: FutureFork = 3 paths.
Consent & Disclosure / सहमति और खुलासा (यूज़र को बताकर अनुमति लेना) TellThenUse™ (pehle batao, fir use)
HINDI: Consent & Disclosure का मतलब है users को data use और AI involvement के बारे में स्पष्ट रूप से बताना और जरूरत होने पर उनकी अनुमति लेना। Users को यह पता होना चाहिए कि कौन-सा data collect हो रहा है, क्यों collect हो रहा है, और कितने समय तक रखा जाएगा। Clear disclosure surprise कम करता है, trust बढ़ाता है, और ethical data handling को support करता है।
ENGLISH: Consent and disclosure mean informing users about data use and AI involvement, and obtaining permission when required. Users should clearly know what data is collected, why it is needed, and how long it will be retained. Clear disclosure reduces surprise, builds trust, and supports ethical and responsible data handling in AI systems.
HINGLISH: TellThenUse™ (pehle batao, fir use) TellThenUse™ = pehle inform, phir collect. Agar bina bataye data liya gaya, to trust turant toot jata hai.
Day-to-day example: App permissions—camera ya mic access—user ko clearly bataya jata hai ki kyun chahiye.
Anchor hook: “No surprise privacy.”
Recall key: TellThenUse = disclose → consent → control.
Audit Trail / ऑडिट ट्रेल (क्या बदला, कब, किसने) ProofLog™ (change ka record)
HINDI: Audit Trail AI prompts और outputs से जुड़े changes, versions, approvals, और incidents का पूरा रिकॉर्ड होता है। यह accountability तय करने, debugging करने, compliance review करने, और failures से सीखने में मदद करता है। एक मजबूत audit trail में timestamps, owners, change reasons, और test results शामिल होते हैं।
ENGLISH: An audit trail is a structured record of changes, versions, approvals, and incidents related to AI prompts and outputs. It enables accountability, debugging, compliance review, and systematic learning from failures. A strong audit trail captures timestamps, owners, reasons for change, and associated test results.
HINGLISH: ProofLog™ (change ka record) ProofLog™ = “proof ka register.” Agar output bigad gaya, to turant pata chale: kaunsa version, kisne change kiya, aur kyun.
nDay-to-day example: Bank passbook ya statement—har transaction ka complete record.
Anchor hook: “No logs = no proof.”
Recall key: ProofLog = who + what + when + why.
Continuous Improvement / निरंतर सुधार (फीडबैक से सुधारते रहना) BetterEveryDay™ (feedback → fix)
HINDI: Continuous Improvement का अर्थ है monitoring data, user feedback, और test results के आधार पर prompts और safeguards को लगातार बेहतर बनाना। इससे बार-बार होने वाली failures कम होती हैं और system बदलते context के अनुसार adapt होता है। Ethical improvement में harm signals को track करना और cosmetic changes से पहले safety fixes को प्राथमिकता देना शामिल है।
ENGLISH: Continuous improvement is the ongoing process of refining prompts, controls, and safeguards using monitoring data, user feedback, and test results. It reduces repeated failures, adapts systems to changing contexts, and prioritizes safety and reliability fixes over cosmetic changes to ensure ethical, long-term AI performance.
HINGLISH: BetterEveryDay™ BetterEveryDay™ = feedback ko ignore mat karo. Har complaint ek signal hota hai. Pehle safety aur reliability fix karo, phir style aur polish.
Day-to-day example: Dukan me customers bole ‘packing weak’—next batch me packaging improve kar di.
Anchor hook: “Feedback = fuel.
Recall key: BetterEveryDay = monitor → learn → patch.
Ethics Scorecard / एथिक्स स्कोरकार्ड (FUTURE के हिसाब से स्कोरिंग) EthicsMarks™ (FUTURE pe score)
HINDI: Ethics Scorecard AI systems को FUTURE model जैसे ethical criteria के आधार पर evaluate करने का एक structured तरीका है। इसमें हर category के लिए score, supporting evidence, और action items तय किए जाते हैं। Scorecards abstract ethics को measurable checks में बदलते हैं और teams के बीच consistent governance सुनिश्चित करते हैं।
ENGLISH: An ethics scorecard is a structured evaluation framework that measures an AI system against defined ethical criteria such as the FUTURE model. It assigns scores, supporting evidence, and corrective actions for each category, converting abstract ethical principles into measurable, auditable checks that enable consistent governance across teams.
HINGLISH: EthicsMarks™ = ethics ko numbers me badal do taaki debate kam ho aur action zyada. FUTURE ke har letter par rating + proof hota hai.
Day-to-day example: School report card—har subject ke marks aur remarks.
Anchor hook: “Ethics without measurement = opinion.
Recall key: EthicsMarks = score + evidence + fix plan.
आपने यह PromptOps Glossary Complete कर लिया 🎯
B-30 BHARAT AI Education Badge – Level 2 Reward
आपने 60+ Advanced AI terms को Hindi → English → Hinglish में HCAM™ (Hinglish Cognitive Anchoring Model™) के साथ समझकर यह glossary पूरा किया है. यह casual scrolling नहीं था - यह आपकी AI vocabulary + recall discipline का पहला solid, verifiable proof है.
Badge का असली अर्थ:
आपने “AI terms समझना” वाला phase पार कर लिया है -
अब आप “AI terms याद रखना + apply करना” वाले learning loop में enter कर चुके हैं.
Invitation to Participate
यह Knowledge Graph एक Living Document है -BFSI और AI Literacy को Hindi + English + Hinglish में accessible बनाने के बड़े मिशन का हिस्सा। GurukulOnRoad और GurukulAI Thought Lab आपको इस प्रयास का हिस्सा बनने के लिए आमंत्रित करते हैं।
- नए BFSI / AI terms सुझाएँ
- Existing पर बेहतर Hinglish mental anchors सुझाएँ
- Exam-oriented definitions improve करने में मदद करें
- Language-first learning को आगे बढ़ाने में योगदान दें
आपकी suggestions इस knowledge graph को और भी accurate, accessible, और Bharat-friendly बनाएंगी। यह पहल B-30 Bharat Financial Education और B-30 Bharat AI Education mission का एक महत्वपूर्ण हिस्सा है।
How can I contribute to the HCAM-KG™ Hinglish Knowledge Graph?
A: 1. Identify BFSI/AI term → 2. Write EN/HI/Hinglish → 3. Submit via contact form → 4. GurukulAI reviews & integrates.
Research & Collaboration Invitation
We also invite researchers in linguistics, cognitive psychology, education, and AI ethics to collaborate on the HCAM-KG™ - Bharat’s Hinglish Knowledge Graph for BFSI & AI Literacy. Your insights on bilingual cognition, Hinglish usage, learning behaviour in B-30 Bharat, और human–AI interaction can help us make this framework even more robust and research-grounded.
- Study how learners process Hindi + English + Hinglish tri-layer definitions
- Analyse HCAM™ - Hinglish Cognitive Anchoring Model™ as a language-first pedagogy
- Explore motivation, confidence, और exam performance in B-30 Bharat learners
- Co-develop working papers, case studies, या pilots on AI-assisted learning in BFSI & AI Literacy
If you are a researcher, faculty member, or doctoral scholar and would like to explore joint research, field studies, या conceptual papers around Hinglish cognition, BFSI literacy, या AI-in-education, we would be happy to hear from you.
GurukulAI Thought Lab Training Programs
Below are the training programs from the book ecosystem. (Aliases remain as program names, as requested.)
FutureScript™
Description: A foresight workshop for thought leaders - exploring post-prompt systems, goal-spec AI, and cognitive twin models.
Coverage: The first program where leaders co-design the AI future narrative. Scenario planning, post-prompt demos, ethical guardrails.
PsyOps Detox Lab™
Description: For leaders, educators, and influencers to understand how AI, narratives, and manipulation loops work - and how to deprogram them.
Coverage: Merges psychological clarity with AI literacy. Cognitive bias demos, loop-breaking prompts, responsible storytelling.
AI Explorer’s Quest™
Description: A gamified program that teaches students AI literacy, prompt basics, and ethical awareness through challenges and storytelling.
Coverage: The first “AI adventure” for young minds, building curiosity + responsibility. Prompt basics, ethical dilemmas, creative AI storytelling, mini projects.
PromptOps for Compliance Commanders
Description: Equip compliance teams with AI-driven prompts that detect risks, flag anomalies, and auto-draft compliance reports.
Coverage: The first training that treats BFSI compliance as a PromptOps system, not a manual checklist. Golden set testing, regulatory red-teaming, AML/KYC prompts, audit trail design.
Risk Mirror AI™
Description: A hands-on workshop where financial professionals use AI as a “mirror” to uncover hidden risk exposures in contracts, credit, and investments.
Coverage: Redefining risk analysis by combining AI outputs with human judgment. Agent chains for risk analysis, bias checks, scenario simulations.
Investor Trust Playbook
Description: Training financial advisors to design AI prompts that build investor trust by balancing optimism with transparency.
Coverage: The first AI training that applies psychology-for-trust models in financial advisory. Framing prompts, authority role prompts, empathy-driven outputs.
LearnScape AI™
Description: Teachers learn to design adaptive lesson prompts that evolve with student responses.
Coverage: The first training for educators on prompt-driven adaptive learning. Goal-spec lesson design, multimodal education prompts, quiz adaptivity.
Exam Navigator AI™
Description: A program for universities to design AI proctors and evaluators for fair assessment.
Coverage: Future-proofing exams against AI misuse and bias. AI proctor prompts, plagiarism detectors, fairness evaluators.
EduTrust Framework™
Description: Training school leaders on balancing AI adoption with parent/student trust.
Coverage: Making schools AI-ready and human-centered. Transparency prompts, parental communication strategies, trust-building narratives.
AdAlchemy AI™
Description: Marketers learn how to design AI prompts that transform raw ideas into tested campaigns with measurable ROI.
Coverage: The alchemy of turning data into persuasion. Copywriter + Designer + Analyst multi-agent workflow.
Customer SoulSignals™
Description: Workshop on prompts that decode emotional tone from customer feedback and generate empathetic responses.
Coverage: The first CX workshop blending prompt engineering with emotional analytics. Sentiment extraction, empathy layering, personalization prompts.
Brand Trust Engine™
Description: Training brand leaders to use psychology-for-trust prompts to strengthen loyalty.
Coverage: A trust-focused AI adoption framework for marketing. Authority bias, transparency prompts, narrative control.
ClauseForge AI™
Description: Law firms learn to design prompt chains that extract, analyze, and benchmark clauses automatically.
Coverage: Where AI meets contract intelligence. Extractor → Analyzer → Summarizer multi-agent pipeline.
Ethics Sentinel AI™
Description: Red-team style workshop for in-house counsel to harden corporate AI against manipulation.
Coverage: The first legal workshop blending ethics + red-teaming. Jailbreak defense, compliance guardrails, transparent audit logging.
SymptomFlow AI™
Description: Doctors, nurses, and triage staff learn to use AI agents that track symptoms over time with memory-enabled prompts.
Coverage: From symptom reporting to symptom storytelling with reliable escalation. Voice-to-text prompts, memory agents, red-team testing for safety.
CareCompass AI™
Description: Empathy-focused AI prompts for patient support, improving communication without losing medical accuracy.
Coverage: The first healthcare AI workshop that measures empathy as a metric. Empathy layering, safety disclaimers, clarity scaffolding.
MedOps Shield™
Description: AI safety drills for healthcare staff - learning how to red-team prompts that could hallucinate dangerous advice.
Coverage: Turning frontline professionals into safety gatekeepers. Adversarial prompts, refusal protocols, compliance logging.
No comments:
Post a Comment