How to Use Conversational Prompt Engineering

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?

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. ♻️

I recently went through the Prompt Engineering guide by Lee Boonstra from Google, and it offers valuable, practical insights. It confirms that getting the best results from LLMs is an iterative engineering process, not just casual conversation. Here are some key takeaways I found particularly impactful: 1. 𝐈𝐭'𝐬 𝐌𝐨𝐫𝐞 𝐓𝐡𝐚𝐧 𝐉𝐮𝐬𝐭 𝐖𝐨𝐫𝐝𝐬: Effective prompting goes beyond the text input. Configuring model parameters like Temperature (for creativity vs. determinism), Top-K/Top-P (for sampling control), and Output Length is crucial for tailoring the response to your specific needs. 2. 𝐆𝐮𝐢𝐝𝐚𝐧𝐜𝐞 𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬: Zero-shot, One-shot, and Few-shot prompting aren't just academic terms. Providing clear examples within your prompt is one of the most powerful ways to guide the LLM on desired output format, style, and structure, especially for tasks like classification or structured data generation (e.g., JSON). 3. 𝐔𝐧𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠: Techniques like Chain of Thought (CoT) prompting – asking the model to 'think step-by-step' – significantly improve performance on complex tasks requiring reasoning (logic, math). Similarly, Step-back prompting (considering general principles first) enhances robustness. 4. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐚𝐧𝐝 𝐑𝐨𝐥𝐞𝐬 𝐌𝐚𝐭𝐭𝐞𝐫: Explicitly defining the System's overall purpose, providing relevant Context, or assigning a specific Role (e.g., "Act as a senior software architect reviewing this code") dramatically shapes the relevance and tone of the output. 5. 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐟𝐨𝐫 𝐂𝐨𝐝𝐞: The guide highlights practical applications for developers, including generating code snippets, explaining complex codebases, translating between languages, and even debugging/reviewing code – potential productivity boosters. 6. 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐚𝐫𝐞 𝐊𝐞𝐲: Specificity: Clearly define the desired output. Ambiguity leads to generic results. Instructions > Constraints: Focus on telling the model what to do rather than just what not to do. Iteration & Documentation: This is critical. Documenting prompt versions, configurations, and outcomes (using a structured template, like the one suggested) is essential for learning, debugging, and reproducing results. Understanding these techniques allows us to move beyond basic interactions and truly leverage the power of LLMs. What are your go-to prompt engineering techniques or best practices? Let's discuss! #PromptEngineering #AI #LLM

Most people prompt every AI the same way. That’s why their outputs are mediocre. I’ve tested hundreds of prompts across every major AI platform. The difference between average and exceptional outputs isn’t prompt length. It’s prompt style matched to the tool. This framework breaks it down: ChatGPT → Prompt like an instructor. Start with a role assignment: “Act as a productivity coach.” Define the specific task. Ask for step-by-step action plans with timelines. Specify your desired format—table, outline, bullet list. Request tool recommendations. ChatGPT excels at structured guidance and task planning. Give it constraints and it delivers. Perplexity → Prompt like a research analyst. Lead with specific information requests. Include relevant keywords, timeframes, and geographies. Ask for cited sources and reference links for verification. Request trend summaries with citations. Follow up with comparison questions that require data-backed reasoning. Perplexity is built for evidence-based analysis. Treat it like a junior analyst who needs clear research parameters. Grok → Prompt like a candid friend. Use conversational tone: “Hey Grok, what do you think about…” Add emotional context. Ask for honest, unfiltered feedback and alternative perspectives. Request comparisons or opposing viewpoints to challenge your assumptions. Ask for common pitfalls and mistakes to avoid. Grok thrives on casual brainstorming and identifying blind spots others miss. Gemini → Prompt like a project planner. Explain the overall project goal upfront. Define expected outputs—tasks, subtasks, timelines. Ask about Google Workspace integrations. Request detailed weekly or daily action plans. Ask for dependency breakdowns and milestones. Request formatted outputs like tables and charts. Gemini is optimized for project management and collaborative workflows. Why this matters: Each model has a personality bias baked into its training data and architecture. ChatGPT leans toward structured helpfulness. Perplexity toward verification and sourcing. Grok toward irreverence and contrarianism. Gemini toward organizational workflows. When you fight these tendencies, you get generic outputs. When you lean into them, you unlock capabilities most users never see. The tactical shift: Stop copying prompts between platforms. Start adapting your communication style to each tool’s strengths. Same question, different framing = dramatically different quality. One prompt style for all tools is lazy. Adapted prompting is leverage.

Most people are using GenAI wrong. They ask one-shot questions and expect magic. If you want real results, that are more relevant, thoughtful, and useful, then you need to prompt better. Here are two advanced prompting patterns that dramatically improve output from any major GenAI chatbot (ChatGPT, Claude, Gemini, Copilot, etc.). These patterns work across them all. CHAIN-OF-THOUGHT PATTERN - Get the model to “think out loud” by breaking down its reasoning into clear, logical steps before giving an answer. Use cases: math, logic, pricing, diagnostics, and planning. Steps: * Use cues like “Let’s work this out step by step." * Optionally include an example (few-shot) or let it figure it out (zero-shot). ✔️ Pros: Improves accuracy and transparency. ❌ Cons: Slower, and if the first step is wrong, the rest often is. TREE-OF-THOUGHT PATTERN - Structure your prompt so the model explores multiple paths or ideas, then compares and converges on the best option. Use cases: root cause analysis, strategic decisions, and product ideas. Steps: * Ask it to explore different possibilities. * Have it compare them. * Ask for a final recommendation. ✔️ Pros: Encourages critical thinking and creativity. ❌ Cons: Verbose, computationally heavy, may overthink. Most people stop at the first answer. These techniques push the model to do more: to reason, refine, and iterate. Prompt smarter. Get better results. #PromptEngineering #GenerativeAI #ChatGPT #AIProductivity #WorkSmarter #AdvancedPrompts #AIChatbots #LLMs #AIForWork

When it comes to building truly reliable AI agents, I’ve realized that prompting isn’t just about giving instructions, it’s about crafting intentional conversations that guide the model with clarity, structure, and context. These prompt engineering techniques have shaped the way we should think about deploying LLM-powered systems in the real world. The goal isn’t just output, it’s precision, traceability, and contextual awareness baked into every generation It starts with being hyper-specific and detailed—think of your LLM like a new team member. The clearer you are about their task, constraints, and tone, the better they perform. Pair that with persona prompting to set the right expectations, and suddenly your LLM behaves more like a domain expert than a chatbot. From there, you outline the task and give it a plan, making even the most complex workflows feel digestible for the model. Structuring the prompt with bullet points, Markdown, or even XML-like tags makes the output predictable and parseable, especially when dealing with automation pipelines. I often add few-shot examples directly in the prompt to guide the model with real-world context. These examples anchor behavior and dramatically reduce misunderstanding. Things really start to scale with prompt folding and dynamic generation. In multi-stage flows, I let earlier outputs shape the next prompt. It’s how you make agents more adaptive. Still, I always include an escape hatch—asking the LLM to admit when it doesn't know something. It’s a small tweak that prevents hallucinations and builds trust. For deeper insight, I include debug info or thinking traces. Asking the LLM to explain its logic is like reading the footnotes of its thought process—great for debugging and refinement. But the real crown jewel? Your eval suite. Prompting without evaluation is like flying blind. Having test cases lets you track improvements, regressions, and stability across iterations. Finally, LLM personalities and distillation matter more than people think. Some models need more hand-holding; others just “get it.” I often use a bigger model to refine prompts and then distill them down for faster, cheaper inference with smaller models. Building reliable AI agents, don’t overlook the prompt. Get intentional, get structured.

Want to use GPT or Claude to help with something complicated and loosely defined — like building a comms plan for a company-wide initiative? Here’s a pattern that leveled up my prompt-fu like there's no tomorrow. ✅ Step 1: Set the stage, don’t trigger the model (yet) “I’m working on [insert project]. I’ll upload the background material. Don’t do anything until I say I’m ready and give you further instructions.” This gives the model time to ingest, not assume. If you don't do this, it’ll start guessing what you want — and usually guess wrong. This saves me tons of backtracking. ✅ Step 2: Kick off the interaction with clear context and a defined role “You’re an internal comms consultant helping the Chief Product & Tech Officer of a public company roll out a major change initiative. Interview me one question at a time until you’re 95% sure you have what you need.” This flips the default dynamic. Instead of hallucinating, the model starts by asking smart, clarifying questions — and only switches to generation once it knows enough to do the job right. This simple two-step pattern has leveled up how I work with LLMs — especially on open-ended, executive-level tasks. 🚀 It’s cut out something like 95% of my frustration with these tools. Curious if others are doing something similar — or better? What’s your go-to prompting move? #promptengineering #worksmarter #LLM #AIworkflow

Monday Technical Deep Dive: Prompting for Precision You've probably heard about AI everywhere, but are you prompting it right to get the best results? Getting useful output from models like Gemini or ChatGPT isn't magic; it's a skill called Prompt Engineering. If your prompt is weak, your output will be too. I recently attended Google’s Generative AI Leader Program and solidified a core principle: Better Inputs = Better Outputs. Here are three simple techniques to immediately improve your results: 1. Zero-Shot Prompting (The Baseline) This is the simplest approach. You give the model no examples, just the instruction. Example: "Explain the concept of API idempotency." When to use it: For basic questions, definitions, or tasks where the model already has extensive knowledge. It's your starting point. 2. Few-Shot Prompting (The Teacher) This is where you give the model a few examples of the desired input/output format before asking your actual question. You are essentially teaching it your style. Example: "Here are three examples of how I write a professional email closing: [Example 1], [Example 2], [Example 3]. Now, write an email to a recruiter following this style." When to use it: When the output needs to match a specific format, tone, or structure (e.g., code functions, marketing copy, or technical documentation). 3. Chain-of-Thought (CoT) Prompting (The Analyst) This is the most powerful technique for complex tasks. You instruct the model to explain its reasoning step-by-step before providing the final answer. Example: "Before giving the final answer, first list and explain the security risks associated with deploying this new cloud function. Then, suggest three mitigation strategies." When to use it: For complex analysis, multi-step problem-solving, or debugging. For me, this is essential when working on AI and Security concepts, as you need verifiable reasoning. Prompting is a skill that will only grow in importance. Which of these techniques are you going to test today? Let me know your results! #GenerativeAI #PromptEngineering #TechnicalDeepDive #SoftwareEngineering #AI

Prompt Engineering in 2025: The Skills Every AI Professional Must Master Prompt Engineering is no longer just a “nice-to-have”—it’s a core capability for AI Product Managers, Data Leaders, and anyone building with LLMs. According to Google’s Prompt Engineering guide writing effective prompts is an iterative discipline, and the difference between an average prompt and a great one can determine accuracy, creativity, cost, and safety of AI systems. Here are the essentials every professional should know: 🔹 1. Master LLM Output Controls The guide strongly emphasizes tuning model configurations—not just the prompt. Key levers include: ◾ Temperature → controls randomness ◾ Top-K / Top-P → controls diversity ◾ Max Tokens → controls cost + verbosity 🔹 2. Use Powerful Prompting Techniques Modern prompting goes far beyond simple instructions. Top techniques highlighted in the guide: ◾ Zero-shot / One-shot / Few-shot examples ◾ System + Role + Context prompts ◾ Chain of Thought (CoT) for reasoning ◾ Step-Back Prompting for better accuracy ◾ ReAct for agentic behavior (reason + act) ◾ Tree of Thoughts for multi-path reasoning Automatic Prompt Engineering (APE) for self-improving prompts 🔹 3. Best Practices for Writing Better Prompts Directly from the guide’s recommendations: ◾ Keep prompts simple, specific, and explicit. ◾ Use instructions (“Do X”) instead of constraints (“Don’t do Y”). ◾ Provide clear examples, especially for structured outputs like JSON. ◾ Use variables in prompts for reusability. ◾ Mix examples to prevent pattern-bias in classification tasks. Treat prompt design as an experiment-driven process: document, iterate, refine. 🔹 4. Code, Debugging & Multimodal Prompts Beyond text, modern LLMs can: ◾ Generate and explain code ◾ Translate code (e.g., Bash → Python) ◾ Debug broken scripts ◾ Interpret images, UI layouts, and more Writing effective prompts unlocks the model’s full multimodal capability. From temperature tuning to Chain-of-Thought, Step-Back reasoning, and ReAct agents — mastering prompts is now essential for building accurate, safe, and reliable AI systems. #PromptEngineering #GenerativeAI #AIProductManagement #LLM #AIAgents #VertexAI #GoogleAI #ArtificialIntelligence #AIMastery #TechLeadership

I spent 1,000+ hours figuring out Prompt Engineering Here's everything I learned distilled into 12 rules you can use right now: 1️⃣ Understand the tool A prompt is how you talk to a language model. Better input = better output 2️⃣ Choose your model wisely GPT-4, Claude, Gemini—each has strengths. Know your tools 3️⃣ Use the right technique ↳ Zero-shot: ask directly ↳ Few-shot: show examples ↳ Chain-of-thought: guide the model step by step 4️⃣ Control the vibe Tune temperature, top-p and max tokens to shape output 5️⃣ Be specific Vagueness kills good output. Say exactly what you want 6️⃣ Context is king Add details, background, goals, constraints—treat it like briefing a world-class assistant 7️⃣ Iterate like crazy Great prompts aren’t written once—they’re rewritten 8️⃣ Give examples Format, tone, structure—show what you want 9️⃣ Think in turns Build multi-step conversations. Follow up, refine, go deeper 🔟 Avoid traps ↳ Too vague → garbage ↳ Too long → confusion ↳ Too complex → derailment ↳ Biased input → biased output 1️⃣1️⃣ One size fits none Customize prompts by task—writing, coding, summarizing, support, etc. 1️⃣2️⃣ Structure is Your Friend: Use headings, bullets, XML tags, or delimiters (like ```) to guide the LLM's focus Mastering these isn't optional—it's how you unlock the *real* power of AI. It's leverage. Which rule do you see people ignore the MOST? 👇 Repost this to help someone level up their prompting game! ♻️