AI Prompt Improvement

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

Prompt engineering is the new consulting superpower. Most haven't realized it yet. Over the last couple of days, I reviewed the latest guides by Google, Anthropic and OpenAI. Some of the key recommendations to improve output: → Being very specific about expertise levels requested → Using structured instructions or meta prompts → Explicitly referencing project documents in the prompt → Asking the model to "think step by step" Based on the guides, here are four ways to immediately level up your prompting skill set as a consultant: 1. Define the expert persona precisely "You're a specialist with 15 years in retail supply chain optimization who has worked with Target and Walmart." Why it matters: The model draws from deeper technical patterns, not just general concepts. 2. Structure the deliverable explicitly "Provide 3 key insights, their implications and then support each with data-driven evidence." Why it matters: This gives me structured material that needs minimal editing. 3. Set distinctive success parameters "Focus on operational inefficiencies that competitors typically overlook." Why it matters: You push the model beyond obvious answers to genuine competitive insights. 4. Establish the decision context "This is for a CEO with a risk-averse investor applying pressure to improve their gross margins." Why it matters: The recommendations align with stakeholder realities and urgency. The above were the main takeaways I took from the guides which I found helpful. When you run these prompts versus generic statements, you will see a massive difference in quality and relevance. Bonus tips which are working for me: → Create prompt templates using the four elements → Test different expert personas against the same problem (I regularly use "Senior McKinsey partner" to counter my position detecting gaps in my thinking.) → Ask the model to identify contradictions or gaps in the data before finalizing any recommendations. We’re only scratching the surface of what these “intelligence partners” can offer. Getting better at prompting may be one of the most asymmetric skill opportunities all of us have today. Share your favourite prompting tip below! P.S Was this post helpful? Should I share one post per week on how I’m improving my AI-related skills?

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.

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.

Anthropic’s “Prompting 101” is one of the best real world tutorials I’ve seen lately on how to actually build a great prompt. Not a toy example. They showcase a real task: analyzing handwritten Swedish car accident forms. Here’s the breakdown: 𝟭. 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗶𝘀 𝗶𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲. You don’t write the perfect prompt on the first try. You test, observe, refine. Just like any other product loop. 𝟮. 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗺𝗮𝘁𝘁𝗲𝗿𝘀. The best prompts follow a playbook: - Start with task + tone context - Load static knowledge into the system prompt - Give clear rules and step-by-step instructions - Show concrete examples - Ask the model to think step-by-step - Define structured output 𝟯. 𝗗𝗼𝗻’𝘁 𝘁𝗿𝘂𝘀𝘁 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹, 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗱𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗶𝘁. In the first version, Claude hallucinated a skiing accident. Only after adding context, rules, and constraints did it produce reliable results. You wouldn’t let a junior analyst guess on regulatory filings. Don’t let your LLM do it either. 𝟰. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗺𝗽𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲. In traditional software, interfaces are buttons and APIs. In GenAI, the interface is language. Your prompt is the program. Most teams still treat prompts like notes in a playground. High-performing teams treat them like production code. That's why in our IBM watsonx platform, prompts are assets just like code or data. 👉Access the video tutorial here: https://lnkd.in/gUdHc2uy ________________________ ♻️ Repost to share these insights! ➕ Follow Armand Ruiz for more

Most people don’t realize: AI can coach you on how to prompt it better. Here’s how to turn AI into your personal prompt coach, so you get better results and learn how to use AI faster. Try this two-step fix: 1. State your goal and context. 2. Ask one of these questions: ➡️ "How would you rewrite my prompt to get more [specific, creative, detailed, etc.] responses?" ➡️ "If you were trying to get [desired outcome], how would you modify this prompt?" ➡️ "If this were your prompt, what would you change to make it more effective?" ➡️ "What elements are missing from my prompt that would help you generate better responses?" ➡️ "How might you enhance this prompt to avoid common pitfalls or misinterpretations?" ➡️ Or simply: "Improve my prompt." Before: "Explain force majeure clauses." After: "Analyze how courts in California have interpreted force majeure clauses in commercial leases since COVID-19, focusing on what constitutes 'unforeseeable circumstances' and the burden of proof required to invoke these provisions." The difference? A broad, non-jx specific, superficial overview vs. actionable legal insights for commercial leases in California. Not only will you get better outcomes, but you will learn how to improve your prompting in the process. What are your go-to strategies or favorite prompts to optimize AI responses?

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. ♻️

Prompting is not about typing better sentences. It’s about transferring intent clearly. When AI outputs feel off, incomplete, or confusing, the issue is rarely intelligence. It’s almost always a gap in instruction - missing context, unclear goals, or poorly defined boundaries. This guide lays out 20 practical rules of prompt engineering that address exactly those gaps. It shows how small changes in how you ask can completely change what you get back. The framework covers how to: - Clearly define what you want and why you’re asking - Assign the right role so the model responds from the correct perspective - Provide context that removes assumptions and guesswork - Control structure, tone, and level of detail in advance - Break complex requests into smaller, sequential steps - Use examples to anchor expectations instead of hoping the model guesses - Apply constraints to reduce fluff, repetition, and irrelevant output - Iterate deliberately instead of rewriting prompts from scratch - Validate responses and catch logical gaps early These rules don’t make prompts longer. They make them more intentional. Once you apply this approach, AI stops feeling unpredictable. Responses become more consistent, more usable, and closer to what you actually had in mind. Prompting then shifts from trial-and-error to a repeatable workflow - one you can rely on for writing, analysis, coding, planning, and decision support. If AI is part of how you think and work, this kind of structure quietly improves everything that comes after. Would love to know which of these rules you already use and which ones surprised you.

Most people blame AI for bad output. Usually, the problem starts earlier. AI does not create standards on its own. It reflects the quality of the input, the clarity of the thinking, and the intent behind the prompt. If you squeeze a lemon, you get lemon juice. If you run a rushed brief, a vague prompt, or sloppy thinking through an AI system, you usually get the same thing back in a cleaner format. That is why so much AI content feels empty. The machine did not invent the emptiness. It processed it. The same pattern shows up in people and teams. When people work with clarity, discipline, and trust, the output tends to improve. When they work in confusion, haste, and suspicion, that spreads too. AI amplifies what it is given. So this is not just a prompting problem. It is a human one. Good prompting matters, but good prompting is really just clear thinking made visible. What is the task? Who is it for? What does good look like? What should stay out? What context actually matters? If you cannot answer those questions, the model fills the gaps with guesses. And a guessed answer, no matter how fluent, is still a guess. That is the deeper issue. If teams hide context, chase speed, and skip the thinking, AI will not fix the mess. It will scale it. You cannot build useful AI on top of human confusion. Better thinking in. Better output out. That rule applies to people as much as machines. ♻️ Share if you find this useful. ➕ Follow Jyothish Nair for reflections on AI, change, and human-centred AI #AI  #PromptEngineering

Prompt formatting can have a dramatic impact on LLM performance, but it varies substantially across models. Some pragmatic findings from a recent research paper: 💡 Prompt Format Significantly Affects LLM Performance. Different prompt formats (plain text, Markdown, YAML, JSON) can result in performance variations of up to 40%, depending on the task and model. For instance, GPT-3.5-turbo showed a dramatic performance shift between Markdown and JSON in code translation tasks, while GPT-4 exhibited greater stability. This indicates the importance of testing and optimizing prompts for specific tasks and models. 🛠️ Tailor Formats to Task and Model. Prompt formats like JSON, Markdown, YAML, and plain text yield different performance outcomes across tasks. For instance, GPT-3.5-turbo performed 40% better in JSON for code tasks, while GPT-4 preferred Markdown for reasoning tasks. Test multiple formats early in your process to identify which structure maximizes results for your specific task and model. 📋 Keep Instructions and Context Explicit. Include clear task instructions, persona descriptions, and examples in your prompts. For example, specifying roles (“You are a Python coder”) and output style (“Respond in JSON”) improves model understanding. Consistency in how you frame the task across different formats minimizes confusion and enhances reliability. 📊 Choose Format Based on Data Complexity. For simple tasks, plain text or Markdown often suffices. For structured outputs like programming or translations, formats such as JSON or YAML may perform better. Align the prompt format with the complexity of the expected response to leverage the model’s capabilities fully. 🔄 Iterate and Validate Performance. Run tests with variations in prompt structure to measure impact. Tools like Coefficient of Mean Deviation (CMD) or Intersection-over-Union (IoU) can help quantify performance differences. Start with benchmarks like MMLU or HumanEval to validate consistency and accuracy before deploying at scale. 🚀 Leverage Larger Models for Stability. If working with sensitive tasks requiring consistent outputs, opt for larger models like GPT-4, which show better robustness to format changes. For instance, GPT-4 maintained higher performance consistency across benchmarks compared to GPT-3.5. Link to paper in comments.