Prompt Engineering Strategies for Success

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 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

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. ♻️

Prompt engineering ≠ typing good English Get it wrong and it can break your business I've lost count of how many times I hear: "It's just writing clever instructions" or "You must be ex-OpenAI to do prompt engineering" But real prompt engineering is much more than that. Here is what it actually takes: → Industry standard benchmarking → Legal compliance coordination → Security vulnerability testing → Prompt injection prevention → Safety filter implementation → Multi-step workflow design → Few-shot example libraries → Rate limiting configuration → Conversation log analysis → Conditional logic creation → Token cost optimization → Version control systems → Audit demographic bias → Edge case debugging → User intent mapping → Build testing suites → A/B test execution → API integration testing → Model drift monitoring → Chain-of-thought flows → Team training facilitation → Context window optimization → Fallback mechanism building → Model fine-tuning coordination → Output format standardization → Prompt caching implementation → Design decision documentation → Business requirement translation → Cross-model compatibility testing → Performance monitoring automation → Production deployment orchestration → Stakeholder expectation management Most of this work isn't about crafting clever instructions (though that's part of it). Prompt engineering is invisible until it goes wrong. When done well, the AI "just works." When done poorly? You're looking at hallucinations, bias, security vulnerabilities, and million-dollar failures. Here's the real secret: If you can master this chaos, you become indispensable. You are not just a prompt engineer. You're pure gold. 💭 What's your take? Are you a prompt engineer dealing with these challenges, or do you still think it's "just good communication skills"? ♻️ Repost to help your network achieve success. And follow Basia Kubicka for more.

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

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

𝗙𝗼𝗿𝗴𝗲𝘁 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂’𝘃𝗲 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝗮𝗯𝗼𝘂𝘁 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀 - 𝗶𝘁 𝗱𝗼𝗲𝘀𝗻’𝘁 𝘄𝗼𝗿𝗸 𝗮𝗻𝘆𝗺𝗼𝗿𝗲! 💀 For the past couple of years teaching 𝗚𝗲𝗻𝗔𝗜 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗮𝘁 𝗜𝗻𝘁𝗲𝗹, I've been sharing insights on how to improve and refine 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 - using techniques like 𝗯𝗮𝗰𝗸-𝗮𝗻𝗱-𝗳𝗼𝗿𝘁𝗵 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻𝘀, 𝗖𝗵𝗮𝗶𝗻-𝗼𝗳-𝗧𝗵𝗼𝘂𝗴𝗵𝘁, 𝗮𝗻𝗱 𝗳𝗲𝘄-𝘀𝗵𝗼𝘁 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀. Turns out… 𝘁𝗵𝗼𝘀𝗲 𝗺𝗲𝘁𝗵𝗼𝗱𝘀 𝙘𝙤𝙢𝙥𝙡𝙚𝙩𝙚𝙡𝙮 𝗯𝗿𝗲𝗮𝗸 𝘄𝗶𝘁𝗵 𝗻𝗲𝘄 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀. Models like 𝗢𝟯-𝗺𝗶𝗻𝗶, 𝗢𝟭, 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸 𝗥𝟭 don’t just struggle with "traditional" prompting - they fail spectacularly, returning convoluted, self-contradicting, and often useless results. 💡 𝗧𝗵𝗲 𝗻𝗲𝘄 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵? 𝗦𝘁𝗼𝗽 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝗽𝗿𝗼𝗺𝗽𝘁𝘀. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝗯𝗿𝗶𝗲𝗳𝘀. These models don’t need step-by-step instructions. They 𝗻𝗲𝗲𝗱 𝗿𝗶𝗰𝗵, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 context upfront so they can reason autonomously. 𝗛𝗲𝗿𝗲’𝘀 𝗮 𝗴𝗿𝗲𝗮𝘁 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 𝗳𝗿𝗼𝗺 𝗟𝗮𝘁𝗲𝗻𝘁𝗦𝗽𝗮𝗰𝗲 𝗼𝗻 𝗵𝗼𝘄 𝘁𝗼 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 𝗳𝗼𝗿 𝗢𝟭. Instead of traditional prompt engineering, it focuses on a 𝗴𝗼𝗮𝗹, 𝗿𝗲𝘁𝘂𝗿𝗻 𝗳𝗼𝗿𝗺𝗮𝘁, 𝗮𝗻𝗱 𝗮 𝗿𝗶𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗱𝘂𝗺𝗽.  𝗛𝗼𝘄 𝘁𝗼 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗕𝗲𝘀𝘁 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 𝘄𝗶𝘁𝗵 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀 ✅ Describe WHAT you want (not HOW to do it). ✅ Be ultra-specific about what you want (and don’t want). ✅ Only set a role/persona if absolutely necessary (jury's still out a bit) ✅ No back-and-forth chatting (one master prompt). ✅ No few-shot examples (zero-shot by default). ✅ Provide 10x more context (cut fluff, added value text only). ✅ Put context at the end of the prompt (yes, order matters). 🔗 𝗛𝗶𝗴𝗵𝗹𝘆 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗲𝗱 𝗿𝗲𝗮𝗱𝘀: 📌 LatentSpace: O1 isn't a chat model (and that’s the point): https://lnkd.in/gVtJbDU3 📌 Microsoft: Prompt Engineering for OpenAI’s O1 and O3-mini: https://lnkd.in/g7C8cz8B For my Intel colleagues, join me for more on the latest in class at 𝗴𝗼𝘁𝗼/𝗟𝗲𝗮𝗿𝗻𝗚𝗲𝗻𝗔𝗜. For the rest of my network: 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗮𝗱𝗮𝗽𝘁𝗶𝗻𝗴 𝘁𝗼 𝘁𝗵𝗶𝘀 𝘀𝗵𝗶𝗳𝘁 𝗶𝗻 𝗔𝗜 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗳𝗼𝗿 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀? #AI #GenAI #PromptEngineering #IAmIntel

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.

Here’s my prompt engineering process that’s helped me publish 4,000+ pages and generate 5M+ visitors. I probably wasted over 100 hours on figuring out the best prompting techniques for creating content. Most people think prompt engineering is a complex beast. It's not. Here's a simple, actionable framework: 🧑💼 ROLE: Define who the AI should act as. Make it clear. Is your AI a customer service rep? A writer? A tech support agent? The clearer you are, the better the AI performs. 📚 CONTEXT: Give background information. Don't skimp on details. The more context you provide, the more accurate the AI's response will be. It's like giving your AI a map before sending it on a journey. 📝 TASK: Clearly state what you want the model to do. Vague instructions lead to vague results. Be specific. Do you want an article written? A summary created? A question answered? Spell it out. 👥 AUDIENCE: Specify who the response is for. Who will read the AI's output? Tailor the language and style to suit them. A message for engineers will differ from one for marketers. 🗣️ STYLE AND TONE: Indicate the desired style and tone. Formal or casual? Serious or playful? The tone can make or break the effectiveness of the AI's response. Make your choice and stick to it. 📋 FORMAT: Specify the structure. Do you need a list? A paragraph? A dialogue? Format matters. It provides a framework for the AI to follow, making its output more useful. 🚧 CONSTRAINTS: Mention any limitations or rules. Are there word limits? Specific points to avoid? Constraints help refine the AI's output, ensuring it meets your exact needs. Now that you have the basic framework down, here’s what I do… 1. Go to ChatGPT and start to make each aspect of my prompt better. Like instead of saying “You are an SEO expert” I will go through a whole conversation to make the more detailed and richer in context. 2. Start to introduce context slowly as a conversation, instead of shoving everything into one long prompt. 3. Start to programmatically play with different variations of my prompts holding several things constant. 4. Start to introduce more examples into my flows. Telling is good, explaining is better, showing is best. I play around with where to introduce the different components of my prompts. For example, role is usually best as a system prompt. Constraints and format sometimes need to be spread out into multiple places. PS. I’m hosting a 5-hour workshop this Friday where I'll go way deeper: https://lnkd.in/gGuS-bqY

✨ Prompt Engineering for Developers: it’s less magic more knowledge! Most devs think prompt engineering is just “asking concisely .” It’s not. With codegen models, the difference between a vague request and a structured prompt can be hours of refining. Here’s what actually works when prompting for code: 1. Be Specific About Context • Bad: “Write me a login system.” • Better: “Generate a secure login system in Python using Flask, bcrypt for hashing, and JWT for tokens. Include tests.” 2. Define Constraints Explicitly • Language, libraries, style, performance constraints, test coverage — the model won’t assume them unless you spell it out. 3. Iterate Like You Would With a Junior Engineer • Don’t dump everything in one mega-prompt. Break it into: design → implementation → test generation → refactor. 4. Use Chain-of-Thought For Yourself, Not Just The Model • Walk through requirements in natural language before you ask for code. It guides the model to align with your mental architecture. 5. Always Ask for Verification • Example: “Explain what security risks remain in this code.” or “Write a unit test suite to validate edge cases.” 🔮 Developers who master prompting will outpace those who just “ask models to write code.” It’s less about wording tricks and more about thinking in systems, constraints, and iterations. At some point, prompt engineering won’t be a separate skill. It’ll just be software engineering in the age of AI!