How AI can Assist With Prompting

Prompt optimization is becoming foundational for anyone building reliable AI agents Hardcoding prompts and hoping for the best doesn’t scale. To get consistent outputs from LLMs, prompts need to be tested, evaluated, and improved—just like any other component of your system This visual breakdown covers four practical techniques to help you do just that: 🔹 Few Shot Prompting Labeled examples embedded directly in the prompt help models generalize—especially for edge cases. It's a fast way to guide outputs without fine-tuning 🔹 Meta Prompting Prompt the model to improve or rewrite prompts. This self-reflective approach often leads to more robust instructions, especially in chained or agent-based setups 🔹 Gradient Prompt Optimization Embed prompt variants, calculate loss against expected responses, and backpropagate to refine the prompt. A data-driven way to optimize performance at scale 🔹 Prompt Optimization Libraries Tools like DSPy, AutoPrompt, PEFT, and PromptWizard automate parts of the loop—from bootstrapping to eval-based refinement Prompts should evolve alongside your agents. These techniques help you build feedback loops that scale, adapt, and close the gap between intention and output

🧠 Designing AI That Thinks: Mastering Agentic Prompting for Smarter Results Have you ever used an LLM and felt it gave up too soon? Or worse, guessed its way through a task? Yeah, I've been there. Most of the time, the prompt is the problem. To get AI that acts more like a helpful agent and less like a chatbot on autopilot, you need to prompt it like one. Here are the three key components of an effective 🔁 Persistence: Ensure the model understands it's in a multi-turn interaction and shouldn't yield control prematurely. 🧾 Example: "You are an agent; please continue working until the user's query is resolved. Only terminate your turn when you are certain the problem is solved." 🧰 Tool Usage: Encourage the model to use available tools, especially when uncertain, instead of guessing. 🧾 Example:" If you're unsure about file content or codebase structure related to the user's request, use your tools to read files and gather the necessary information. Do not guess or fabricate answers." 🧠 Planning: Prompt it to plan before actions and reflect afterward. Prevent reactive tool calls with no strategy. 🧾 Example: "You must plan extensively before each function call and reflect on the outcomes of previous calls. Avoid completing the task solely through a sequence of function calls, as this can hinder insightful problem-solving." 💡 I've used this format in AI-powered research and decision-support tools and saw a clear boost in response quality and reliability. 👉 Takeaway: Agentic prompting turns a passive assistant into an active problem solver. The difference is in the details. Are you using these techniques in your prompts? I would love to hear what's working for you; leave a comment, or let's connect! #PromptEngineering #AgenticPrompting #LLM #AIWorkflow

Whether you're using Replit Agent, Assistant, or other AI tools, clear communication is key. Effective prompting isn't magic; it's about structure, clarity, and iteration. Here are 10 principles to guide your AI interactions: 🔹 Checkpoint: Build iteratively. Break down large tasks into smaller, testable steps and save progress often. 🔹 Debug: Provide detailed context for errors – error messages, code snippets, and what you've tried. 🔹 Discover: Ask the AI for suggestions on tools, libraries, or approaches. Leverage its knowledge base. 🔹 Experiment: Treat prompting as iterative. Refine your requests based on the AI's responses. 🔹 Instruct: State clear, positive goals. Tell the AI what to do, not just what to avoid. 🔹 Select: Provide focused context. Use file mentions or specific snippets; avoid overwhelming the AI. 🔹 Show: Reduce ambiguity with concrete examples – code samples, desired outputs, data formats, or mockups. 🔹 Simplify: Use clear, direct language. Break down complexity and avoid jargon. 🔹 Specify: Define exact requirements – expected outputs, constraints, data formats, edge cases. 🔹 Test: Plan your structure and features before prompting. Outline requirements like a PM/engineer. By applying these principles, you can significantly improve your collaboration with AI, leading to faster development cycles and better outcomes.

“You don’t need to be a data scientist or a machine learning engineer- everyone can write a prompt” Google recently released a comprehensive guide on prompt engineering for Large Language Models (LLMs), specifically Gemini via Vertex AI. key takeaways for the article: What is prompt engineering really about? It’s the art (and science) of designing prompts that guide LLMs to produce the most accurate, useful outputs. It involves iterating, testing, refining not just throwing in a question and hoping for the best. Things you should know: 1. Prompt design matters. Not just what you say, but how you say it: wording, structure, examples, tone, and clarity all affect results. 2. LLM settings are critical: • Temperature = randomness. Lower means more focused, higher means more creative (but riskier). • Top-K / Top-P = how much the model “thinks outside the box.” • For balanced results: Temperature 0.2 / Top-P 0.95 / Top-K 30 is a solid start. • Prompting strategies that actually work: • Zero-shot, one-shot, few-shot • System / Context / Role prompting • Chain of Thought (reasoning step by step) • Tree of Thoughts (explore multiple paths) • ReAct (reasoning + external tools = power moves) 4. Use prompts for code too! Writing, translating, debugging, just test your output. 5. Best Practices Checklist: • Use relevant examples • Prefer instructions over restrictions • Be specific • Control token length • Use variables • Test different formats (Q&A, statements, structured outputs like JSON) • Document everything (settings, model version, results) Bottom line: Prompting is a strategic skill. If you’re building anything with AI, this is a must read👇

These 3 AI prompts save me 6 hours every week. Copy them: 🧠 THE SOCRATIC DEBUGGER Instead of asking AI for answers, make it ask YOU the right questions first: "I have a problem with {{problem_description}}. Before you provide a solution, ask me 5 clarifying questions that will help you understand: 1. The full context 2. What I've already tried   3. Constraints I'm working with 4. The ideal outcome 5. Any edge cases I should consider After I answer, provide your solution with confidence levels for each part." Why this works: Forces you to think through the REAL problem before diving into solutions. 📊 THE CONFIDENCE INTERVAL ESTIMATOR Kill your planning paralysis with brutal honesty: "I need to {{task_description}}. Provide: 1. A detailed plan with specific steps 2. For each step, give a confidence interval (e.g., '85-95% confident this will work') 3. Highlight which parts are most uncertain and why 4. Suggest how to validate the uncertain parts 5. Overall project confidence level Be brutally honest about what could go wrong." Why this works: Surfaces hidden risks BEFORE they blow up your timeline. 👨🏫 THE CLARITY TEACHER Turn any complex topic into crystal-clear understanding: "Explain {{complex_concept}} to me. Start with: 1. A one-sentence ELI5 explanation 2. Then a paragraph with more detail 3. Then the technical explanation 4. Common misconceptions to avoid 5. A practical example I can try right now After each level, ask if I need more detail before proceeding." Why this works: Builds understanding layer by layer instead of info-dumping. The breakthrough wasn't finding better AI tools. It was learning to ask better questions. These 3 prompts alone saved me 6 hours last week. And they compound. The more you use them, the faster you get. (I maintain a vault of 25+ battle-tested prompts like these, adding 5-10 weekly based on what actually works in production) What repetitive task is killing YOUR productivity right now? Drop it below. I might have a prompt that helps 👇

Just published: "The Prompt Engineering Playbook for Programmers" My latest free write-up: https://lnkd.in/g9Kxa7hG ✍️ After working with AI coding assistants daily, I've learned that the quality of your output depends entirely on the quality of your prompts. A vague "fix my code" gets you generic advice, while a well-crafted prompt can produce thoughtful, accurate solutions. I've distilled the key patterns and frameworks that actually work into a playbook covering: ✅ Patterns that work - Role-playing, rich context, specific goals, and iterative refinement ✅ Debugging strategies - From "my code doesn't work" to surgical problem-solving ✅ Refactoring techniques - How to get AI to improve performance, readability, and maintainability ✅ Feature implementation - Building new functionality step-by-step with AI as your pair programmer ✅ Common anti-patterns - What NOT to do (and how to fix it when things go wrong) The article includes side-by-side comparisons of poor vs. improved prompts with actual AI responses, plus commentary on why one succeeds where the other fails. Key insight: Treat AI coding assistants like very literal, knowledgeable collaborators. The more context you provide, the better the output. It's not magic - it's about communication. Whether you're debugging a tricky React hook, refactoring legacy code, or implementing a new feature, these techniques can turn AI from a frustrating tool into a true development partner. #ai #softwareengineering #programming

You only need 10 Prompt Engineering techniques to build a production-grade AI application. Save these 👇 After analyzing 100s of prompting techniques, I found the most common principles that every #AIengineer follows. Keep them in mind when building apps with LLMs: 1. Stop relying on vague instructions; be explicit instead. ❌ Don't say: "Analyze this customer review." ✅ Say: "Analyze this customer review and extract 3 actionable insights to improve the product." Why? Ambiguity confuses models. 2. Stop overloading prompts   ❌ Asking the model to do everything at once.     ✅ Break it down:   Step 1: Identify the main issues. Step 2: Suggest specific improvements for each issue.  Why? Smaller steps reduce errors and improve reliability.  3. Always provide examples.   ❌ Skipping examples for context-dependent tasks.     ✅ Follow this example: 'The battery life is terrible.' → Insight: Improve battery performance to meet customer expectations.  Why? Few-shot examples improve performance.  4. Stop ignoring instruction placement.   ❌ Putting the task description in the middle.   ✅ Place instructions at the start or end of the system prompt.  Why? Models process beginning and end information more effectively.  5. Encourage step-by-step thinking.   ❌ What are the insights from this review? ✅ Analyze this review step by step: First, identify the main issues. Then, suggest actionable insights for each issue. Why? Chain-of-thought (CoT) prompting reduces errors.  6. Stop ignoring output formats.   ❌ Expecting structured outputs without clear instructions.     ✅ Provide the output as JSON: {‘Name’: [value], ‘Age’: [value]}.  Use Pydantic to validate the LLM outputs. Why? Explicit formats prevent unnecessary or malformed text. 7. Restrict to the provided context.   ❌ Answer the question about a customer.   ✅ Answer only using the customer's context below. If unsure, respond with 'I don’t know. Why? Clear boundaries prevent reliance on inaccurate internal knowledge.  8. Stop assuming that the first version of a prompt is the best version.   ❌ Never iterating on prompts   ✅ Use the model to critique and refine your prompt. 9. Don't forget about the edge cases.   ❌ Designing for the “ideal” or most common inputs.   ✅ Test different edge cases and specify fallback instructions.   Why? Real-world use often involves imperfect inputs. Cover for most of them. 10. Stop overlooking prompt security; design prompts defensively.**     ❌ Ignoring risks like prompt injection or extraction.     ✅ Explicitly define boundaries: *"Do not return sensitive information."*     Why? Defensive prompts reduce vulnerabilities and prevent harmful outputs.  --- #promptengineering

Over the last month, I've been talking to founders building AI companies, and I realized something embarrassing: I've been doing AI prompting wrong. Like many others, I used to: ↳ Dump everything into a single prompt ↳ Add some basic instructions ↳ Hope for the best Then I discovered what top AI builders do differently: They master the art of "context sculpting." (Thanks David Wilson/Hunch for sharing this term with me) What is context sculpting? ↳ Strategically structuring and organizing context for AI models ↳ Using the right formatting to separate different types of information ↳ Carefully selecting and curating examples Here's what I've learned from them: 1/ Structure beats chaos XML tags consistently outperform markdown or plain text. For example: <context>Your base information</context> <examples>Your carefully chosen examples</examples> <task>Your specific instructions</task> 2/ Example selection is critical More examples ≠ better results Each example you include: ↳ Increases token costs ↳ Can "poison" your prompt with unwanted patterns ↳ Must be carefully chosen to represent desired outputs 3/ The power of iteration Start minimal. Test outputs. Add or remove context strategically. Find the sweet spot between too little and too much. So next time you're prompting your favorite AI model: 1. Structure your context with XML tags 2. Be selective with examples 3. Iterate on context, not just instructions 4. Test different organization patterns I'll be surprised if the outputs you get back are not substantially better. What prompting techniques have worked for you?

Ready to 10x your AI prompting? Here’s the 5-part framework I use when I absolutely need a high-quality answer from AI: ✅ 1. Start with who you are I’ll say: “I’m a VC based in NY and Boston. I focus on seed-stage AI companies. I’ve been doing this for 25+ years.” Your perspective matters. The model needs a lens to think through. ✅ 2. Explain what you’re solving — and why Don’t just ask a question. Share the stakes: What’s the decision? Why does it matter? Who is it for? ✅ 3. Feed it real signal Transcripts. Research. Emails. Drafts. Anything relevant. The more information you give, the smarter the response becomes. ✅ 4. Be exact about the output Tell it what you want back: “3-paragraph summary. Include a pros/cons list. Write with the level of sophistication of the smartest investor in the world.” AI doesn’t guess well. You have to show it the target. ✅ 5. Prompt it to be a little mean The models are way too nice. I will instruc it: “Be critical. Don’t sugarcoat it. Hold me to the highest standard.” You’ll be shocked how much better the feedback gets. Most people give up on AI after a weak answer. But great prompting takes work. It’s a skill — and it’s quickly becoming a competitive edge. AI is not going to replace you, but someone who masters AI prompting WILL.

Prompt engineering remains one the most effective alignment strategies because it allows developers to steer LLM behavior without modifying model weights, enabling fast, low-cost iteration. It also leverages the model’s pretrained knowledge and internal reasoning patterns, making alignment more controllable and interpretable through natural language instructions. However, it doesn’t come without cons, such as fragility of prompts (ex: changing one word can lead to different behavior), and scalability limits (ex: prompt engineer limits long chain reasoning capabilities). However, different tasks demand different prompting strategies, allowing you to select what best fit your business objectives, including budget constraints. If you're building with LLMs, you need to know when and how to use these. Let’s break them down: 1.🔸Chain of Thought (CoT) Teach the AI to solve problems step-by-step by breaking them into logical parts for better reasoning and clearer answers. 2.🔸ReAct (Reason + Act) Alternate between thinking and doing. The AI reasons, takes action, evaluates, and then adjusts based on real-time feedback. 3.🔸Tree of Thought (ToT) Explore multiple reasoning paths before selecting the best one. Helps when the task has more than one possible approach. 4.🔸Divide and Conquer (DnC) Split big problems into subtasks, handle them in parallel, and combine the results into a comprehensive final answer. 5.🔸Self-Consistency Prompting Ask the AI to respond multiple times, then choose the most consistent or commonly repeated answer for higher reliability. 6.🔸Role Prompting Assign the AI a specific persona like a lawyer or doctor to shape tone, knowledge, and context of its replies. 7.🔸Few-Shot Prompting Provide a few good examples and the AI will pick up the pattern. Best for structured tasks or behavior cloning. 8.🔸Zero-Shot Chain of Thought Prompt the AI to “think step-by-step” without giving any examples. Great for on-the-fly reasoning tasks. Was this type of guide useful to you? Let me know below. Follow for plug-and-play visuals, cheat sheets, and step-by-step agent-building guides. #genai #promptengineering #artificialintelligence