AI Prompt Improvement

If you’re an AI engineer trying to understand and build with GenAI, RAG (Retrieval-Augmented Generation) is one of the most essential components to master. It’s the backbone of any LLM system that needs fresh, accurate, and context-aware outputs. Let’s break down how RAG works, step by step, from an engineering lens, not a hype one: 🧠 How RAG Works (Under the Hood) 1. Embed your knowledge base → Start with unstructured sources - docs, PDFs, internal wikis, etc. → Convert them into semantic vector representations using embedding models (e.g., OpenAI, Cohere, or HuggingFace models) → Output: N-dimensional vectors that preserve meaning across contexts 2. Store in a vector database → Use a vector store like Pinecone, Weaviate, or FAISS → Index embeddings to enable fast similarity search (cosine, dot-product, etc.) 3. Query comes in - embed that too → The user prompt is embedded using the same embedding model → Perform a top-k nearest neighbor search to fetch the most relevant document chunks 4. Context injection → Combine retrieved chunks with the user query → Format this into a structured prompt for the generation model (e.g., Mistral, Claude, Llama) 5. Generate the final output → LLM uses both the query and retrieved context to generate a grounded, context-rich response → Minimizes hallucinations and improves factuality at inference time 📚 What changes with RAG? Without RAG: 🧠 “I don’t have data on that.” With RAG: 🤖 “Based on [retrieved source], here’s what’s currently known…” Same model, drastically improved quality. 🔍 Why this matters You need RAG when: → Your data changes daily (support tickets, news, policies) → You can’t afford hallucinations (legal, finance, compliance) → You want your LLMs to access your private knowledge base without retraining It’s the most flexible, production-grade approach to bridge static models with dynamic information. 🛠️ Arvind and I are kicking off a hands-on workshop on RAG This first session is designed for beginner to intermediate practitioners who want to move beyond theory and actually build. Here’s what you’ll learn: → How RAG enhances LLMs with real-time, contextual data → Core concepts: vector DBs, indexing, reranking, fusion → Build a working RAG pipeline using LangChain + Pinecone → Explore no-code/low-code setups and real-world use cases If you're serious about building with LLMs, this is where you start. 📅 Save your seat and join us live: https://lnkd.in/gS_B7_7d

STOP asking ChatGPT to "make it better". Here's how to better prompt it instead: ☑ Clearly Identify the Issue Rather than a vague “make it better,” specify the exact element that needs change. For example: "Rewrite the second paragraph so it includes three concrete examples of our product’s benefits. The tone must be formal and persuasive. Remove any informal language or redundant phrases." ☑ Divide the Task into Discrete Steps Break the overall revision into a sequence of manageable tasks. For example: "Go through my instructions, step by step. – Step 1: Summarize it in one sentence. – Step 2: Identify two specific weaknesses. – Step 3: Rewrite the text to address these weaknesses, incorporating specific data or examples." ☑ Specify the Format and Level of Detail Define exactly how the final output should look. For example: "Provide the final revised text as a numbered list where each item contains 2–3 sentences. Each item must include at least one statistical fact or concrete example, and the overall response should not exceed 250 words." ☑ Request a Chain-of-Thought Explanation Ask the model to detail its reasoning process before giving the final output. For example: "Before providing the final revised text, explain your reasoning step-by-step. Identify which parts need improvement and how your changes will enhance clarity and professionalism. Then, present the final revised version." ☑ Conditional Instructions to Enforce Compliance Add if/then conditions to ensure all requirements are met. For example: "If the revised text does not include at least two concrete examples, then add a sentence with a real-world statistic. Otherwise, finalize the response as is." ☑ Consolidate All Instructions into One Prompt Integrate all the detailed instructions into a single, comprehensive prompt. For example: "First, identify the section of the text that needs improvement and explain why it is lacking. Next, summarize the current text in one sentence and list two specific weaknesses. Then, rewrite the text to address these weaknesses, ensuring the revised version includes three concrete examples, uses a formal and persuasive tone, and is structured as a numbered list with each item containing 2–3 sentences. Each list item must include at least one statistical fact or example, and the overall response must be no longer than 250 words. Before providing the final text, explain your reasoning step-by-step. If the revised text does not include at least two concrete examples, add an additional sentence with a real-world statistic." ___ Why This Works People never give enough context. And once ChatGPT answers, they never correct it enough. Think about it like an intern. Deep prompting is all about precision: give clear instructions, context & the right corrections. PS: Don't forget to use the new o3-mini model. It's crushing any other one. Yes – even DeepSeek.

The interview is for a GenAI Engineer role at Anthropic. Interviewer: "Your prompt gives perfect answers during testing - but fails randomly in production. What’s wrong?" You: “Ah, the prompt drift problem. Identical prompts can yield different outputs due to sampling (temperature/top-p) or shift entirely under paraphrased inputs." Interviewer: "Meaning?" You: "LLMs don't understand instructions - they predict them. A single rephrased sentence, longer context, or slight temperature change can push the model into a different completion path. What looks deterministic in a 10-example test collapses under real-world input diversity." Interviewer: "So how do you fix it?" You: Treat prompts like production code: 1. Prompt templates - lock phrasing with {{placeholders}} for user input. 2. Lock sampling - fix temperature=0, top_p=1 for reproducibility. 3. System-level guardrails - e.g., "Always respond in valid JSON matching this schema: {{schema}}" 4. Fuzz-test inputs - run 1k+ paraphrased variants pre-deploy. 5. Delimiters + structure -> Prevents bleed and enforces parsing: """USER_INPUT: {{input}}""" """OUTPUT_FORMAT: {{schema}}""" Interviewer: "So prompt reliability is more about engineering than creativity?" You: "Exactly. Creative prompting gets you demos. Structured prompting gets you products." Interviewer: "What’s your golden rule for prompt design?" You: “Prompts are code. They need versioning, testing, and regression tracking - not vibes. If you can’t reproduce the output, you can’t trust it." Interviewer: "So prompt drift is basically a reliability bug?" You: "Yes - and fixing it turns GenAI from a prototype into a platform." #PromptEngineering #GenerativeAI

A few months ago, a colleague screamed at Microsoft Copilot like he was auditioning for Bring Me The Horizon. He typed, “Make this into a presentation.” Copilot spat out something. He yelled, “NO, I SAID PROFESSIONAL!” It revised it. Still wrong. “WHY ARE YOU SO STUPID?” And that, dear reader, is when it hit me. It’s not the AI. It’s you. Or rather, your prompts. So, if you've ever felt like ChatGPT, Copilot, Gemini, or any of those AI Agents are more "artificial" than "intelligent"? Then rethink how you’re talking to them. Here are 10 prompt engineering fundamentals that’ll stop you from sounding like you're yelling into the void. 1. Lead with Intent. Start with a clear command: “You are an expert…,” “Generate a monthly report…,” “Translate this to French…" This orients the model instantly. 2. Scope & Constraints First. Define boundaries up front. Length limits, style guides, data sources, even forbidden terms. 3. Format Your Output. Specify JSON schema, markdown headers, or table columns. Models love explicit structure over free form prose. 4. Provide Minimal, High Quality Examples. Two or three exemplar Q→A pairs beat a paragraph of explanation every time. 5. Isolate Subtasks. Break complex workflows into discrete prompts (chain of thought). One prompt per action: analyze, summarize, critique, then assemble. 6. Anchor with Delimiters. Use triple backticks or XML tags to fence inputs. Cuts hallucinations in half. 7. Inject Domain Signals. Name specific frameworks (“Use SWOT analysis,” “Apply the Eisenhower Matrix,” “Leverage Porter’s Five Forces”) to nudge depth. 8. Iterate Rapidly. Version your prompts like code. A/B test variations, track which phrasing yields the cleanest output. 9. Tune the “Why.” Always ask for reasoning steps. Always. 10. Template & Automate. Build parameterized prompt templates in your repo. Still with me? Good. Bonus tips. 1. Token Economy Awareness. Place critical context in the first 200 tokens. Anything beyond 1,500 risks context drift. 2. Temperature vs. Prompt Depth. Higher temperature amplifies creativity. Only if your prompt is concise. Otherwise you get noise. 3. Use “Chain of Questions.” Instead of one long prompt, fire sequential, linked questions. You’ll maintain context and sharpen focus. 4. Mirror the LLM’s Own Language. Scan model outputs for phrasing patterns and reflect those idioms back in your prompts. 5. Treat Prompts as Living Docs. Embed metrics in comments: note output quality, error rates, hallucination frequency. Keep iterating until ROI justifies the effort. And finally, the bit no one wants to hear. You get better at using AI by using AI. Practice like you’re training a dragon. Eventually, it listens. And when it does, it’s magic. You now know more about prompt engineering than 98% of LinkedIn. Which means you should probably repost this. Just saying. ♻️

The new consulting edge isn't AI. It's knowing when your AI is wrong. Every consultant has been there: You ask AI to analyze documents and generate insights. During review, you spot a questionable stat that doesn't exist in the source! AI hallucinations are a problem. The solution? Implementing "prompt evals". → Prompt evals: directions that force AI to verify its own work before responding. A formula for effective evals: 1. Assign a verification role → "Act as a critical fact-checker whose reputation depends on accuracy" 2. Specify what to verify → "Check all revenue projections against the quarterly reports in the appendix" 3. Define success criteria → "Include specific page references for every statistic" 4. Establish clear terminology → "Rate confidence as High/Medium/Low next to each insight" Here is how your prompt will change: OLD: "Analyze these reports and identify opportunities." NEW: "You are a senior analyst known for accuracy. List growth opportunities from the reports. For each insight, match financials to appendix B, match market claims to bibliography sources, add page ref + High/Med/Low confidence, otherwise write REQUIRES VERIFICATION.” Mastering this takes practice, but the results are worth it. What AI leaders know that most don't: "If there is one thing we can teach people, it's that writing evals is probably the most important thing." Mike Krieger, Anthropic CPO By the time most learn basic prompting, leaders will have turned verification into their competitive advantage. Steps to level-up your eval skills: → Log hallucinations in a "failure library" → Create industry-specific eval templates → Test evals with known error examples → Compare verification with competitors Next time you're presented with AI-generated analysis, the most valuable question isn't about the findings themselves, but: 'What evals did you run to verify this?' This simple inquiry will elevate your teams approach to AI & signal that in your organization, accuracy isn't optional.

In the last three months alone, over ten papers outlining novel prompting techniques were published, boosting LLMs’ performance by a substantial margin. Two weeks ago, a groundbreaking paper from Microsoft demonstrated how a well-prompted GPT-4 outperforms Google’s Med-PaLM 2, a specialized medical model, solely through sophisticated prompting techniques. Yet, while our X and LinkedIn feeds buzz with ‘secret prompting tips’, a definitive, research-backed guide aggregating these advanced prompting strategies is hard to come by. This gap prevents LLM developers and everyday users from harnessing these novel frameworks to enhance performance and achieve more accurate results. https://lnkd.in/g7_6eP6y In this AI Tidbits Deep Dive, I outline six of the best and recent prompting methods: (1) EmotionPrompt - inspired by human psychology, this method utilizes emotional stimuli in prompts to gain performance enhancements (2) Optimization by PROmpting (OPRO) - a DeepMind innovation that refines prompts automatically, surpassing human-crafted ones. This paper discovered the “Take a deep breath” instruction that improved LLMs’ performance by 9%. (3) Chain-of-Verification (CoVe) - Meta's novel four-step prompting process that drastically reduces hallucinations and improves factual accuracy (4) System 2 Attention (S2A) - also from Meta, a prompting method that filters out irrelevant details prior to querying the LLM (5) Step-Back Prompting - encouraging LLMs to abstract queries for enhanced reasoning (6) Rephrase and Respond (RaR) - UCLA's method that lets LLMs rephrase queries for better comprehension and response accuracy Understanding the spectrum of available prompting strategies and how to apply them in your app can mean the difference between a production-ready app and a nascent project with untapped potential. Full blog post https://lnkd.in/g7_6eP6y

Telling AI "𝘺𝘰𝘶 𝘢𝘳𝘦 𝘢𝘯 𝘦𝘹𝘱𝘦𝘳𝘵" can make an 𝘓𝘓𝘔 𝘭𝘦𝘴𝘴 𝘢𝘤𝘤𝘶𝘳𝘢𝘵𝘦. A new study tested the most common prompting trick people use: assigning the model an expert persona. The result was surprising: ✅ Expert personas helped with tone, writing, and safety.  ❌ But on factual questions, every version they tested made accuracy worse. On a standard knowledge benchmark, it dropped from 71.6% to 68.0%. 𝗧𝗵𝗲 𝘁𝗿𝗶𝗰𝗸 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗰𝗼𝗽𝗶𝗲𝘀 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲𝗶𝗿 𝘁𝗲𝗺𝗽𝗹𝗮𝘁𝗲𝘀 𝗾𝘂𝗶𝗲𝘁𝗹𝘆 𝗯𝗮𝗰𝗸𝗳𝗶𝗿𝗲𝘀. This is part of why the conversation is shifting from prompt engineering to context engineering. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗮𝗯𝗼𝘂𝘁 𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀. You tell the model how to behave: what role to play, what tone to take, what steps to follow. You write "act as a senior engineer" or "respond formally" and refine the wording until the output looks right. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗮𝗯𝗼𝘂𝘁 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻. Instead of telling the model how to act, you give it what it actually needs to work: your situation, your constraints, your data, examples of what good looks like, and what you want to avoid. The model already knows how to think. What it's missing is your context. Replit Agent 4 is built around this. You describe the outcome, it plans, researches, and builds. What this looks like in practice: → Put the outcome, constraints, and data in one brief, not piecemeal  → Let the agent finish its plan before you step in  → Edit elements directly on the Canvas, not in a new prompt  → Skip reopening long chats for small fixes, it reprocesses everything Get free Replit credits to experiment: https://lnkd.in/gRyC8Mgu Follow for more practical AI tips, Viktoria Semaan #promptengineering #contextengineering #replitpartner #aitips

LLMs are no longer just fancy autocomplete engines. We’re seeing a clear shift—from single-shot prompting to techniques that mimic 𝗮𝗴𝗲𝗻𝗰𝘆: reasoning, retrieving, taking action, and even coordinating across steps. In this visual, I’ve laid out five core prompting strategies: - 𝗥𝗔𝗚 – Brings in external knowledge, enhancing factual accuracy   - 𝗥𝗲𝗔𝗰𝘁 – Enables reasoning 𝗮𝗻𝗱 acting, the essence of agentic behavior   - 𝗗𝗦𝗣 – Adds directional hints through policy models   - 𝗧𝗼𝗧 (𝗧𝗿𝗲𝗲-𝗼𝗳-𝗧𝗵𝗼𝘂𝗴𝗵𝘁) – Simulates branching reasoning paths, like a mini debate inside the LLM   - 𝗖𝗼𝗧 (𝗖𝗵𝗮𝗶𝗻-𝗼𝗳-𝗧𝗵𝗼𝘂𝗴𝗵𝘁) – Breaks down complex thinking into step-by-step logic While not all of these are fully agentic on their own, techniques like 𝗥𝗲𝗔𝗰𝘁 and 𝗧𝗼𝗧 are clear stepping stones to 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 — where autonomous agents can 𝗿𝗲𝗮𝘀𝗼𝗻, 𝗽𝗹𝗮𝗻, 𝗮𝗻𝗱 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁 𝘄𝗶𝘁𝗵 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀. The big picture?  We’re slowly moving from "𝘱𝘳𝘰𝘮𝘱𝘵 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨" to "𝘤𝘰𝘨𝘯𝘪𝘵𝘪𝘷𝘦 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 𝘥𝘦𝘴𝘪𝘨𝘯." And that’s where the real innovation lies.

Most finance professionals still prompt AI like it's Google. Here is my blueprint to prompt like an expert using proven frameworks. #7 is one of my favourites. Also sharing Video guides on AI for Finance here: https://lnkd.in/gQhsAc9e 1. Basic Prompting (CSI + FBI) Stop writing generic requests. Use CSI (Context, Specific, Instruction) to define what you need, and FBI (Format, Blueprint, Identity) to define how it should look. Example: "I am an FP&A Head (Context). My team needs to communicate the monthly forecast update to business leaders (Context). Draft a professional email summarizing the new forecast (Instruction). The tone should be formal but collaborative (FBI)". 2. Chain-of-Thought Complex financial problems require steps, not a single button press. Break your request into sequential queries to guide the AI's reasoning. 👇 Example: "You are helping automate bank reconciliation. First, analyze these transaction examples. Next, determine rules to match them against accounting software. Finally, outline a process for flagging mismatches". 3. Chunking LLMs have character limits. When dealing with large datasets or long reports, break the information into smaller "chunks" for input or request the output in parts. 👇 Example: "My R&D budget has overrun. Break down the potential causes and suggest targeted strategies to address each. Only answer one cause at a time". 4. Explicit Reasoning AI is not a calculator; it predicts text. To fix this, ask the AI to perform the analysis and provide a step-by-step explanation of every formula it used. This allows you to verify the logic before trusting the KPI. 5. Meta-Cognition Reduce hallucinations by asking the AI to self-reflect. Prompt it to describe its reasoning, rate its own response, and identify any biases it might have used. 6. Socratic Prompting Don't settle for the first answer. Ask questions that force the AI to explore the topic deeply or reconsider its assumptions. This is perfect for stress-testing strategy plans. 7. Agent Prompting Define a specific "Identity" for the AI. Instead of a general assistant, tell it: "You are an experienced Financial Analyst with knowledge from top-tier management consulting firms." The output quality changes immediately. I have an Excel file with 50 prompts using these methods. If it sounds useful to you or your team, tell me in the comments and I can send it.

In just a few minutes, here’s one thing you can do to make AI outputs 10x sharper. One of the most common reasons that prompts fail is not because they are too long, but because they lack personal context. And the fastest fix is to dictate your context. Speak for five to ten minutes about the problem, your audience, and the outcome you want, then paste the transcript into your prompt. Next, add your intent and your boundaries in plain language. For example: “I want to advocate for personal healthcare. Keep the tone empowering, not invasive. Do not encourage oversharing. Help people feel supported in the doctor’s office without implying that all responsibility sits on them.” Lastly, tell the model exactly what to produce. You might say: “Draft the first 400 words, include a clear call to action, and give me three title options.” Here’s a mini template: → State who you are and who this is for → Describe your stance and what to emphasize → Add guardrails for tone, privacy, and any “don’ts” → Set constraints like length, format, and voice → Specify the deliverable you want next Until AI memory reliably holds your details, you are responsible for supplying them. Feed the model your story - no need to include PII - to turn generic responses into work that sounds like you.