Tips for Advanced AI Prompting Techniques

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

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?

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.

The Anatomy of a Claude 4.6 Prompt Most people still prompt AI like it's 2023. Here's the framework we at APEX Consulting use every single day: 1. Task Define exactly what you want and what success looks like. "I want to [TASK] so that [SUCCESS CRITERIA]." No role-play instructions. No "act as a senior expert." That phase is over. 2. Context Files Upload files that contain your expertise, standards, and rules. Stop over-explaining in the input box. Put your thinking into structured files. 3. Reference Show the AI exactly what you expect. Upload a concrete example. Define patterns, tone, and structure as explicit rules. Don't say "something like this" and hope it understands. Specify the standard. 4. Brief The only part you write from scratch each time. Everything else lives in files. Define: output type, length, what it must not sound like, and what defines success. Clear constraints produce clear results. 5. Rules Your context file contains your standards, audience, positioning, and taste. Tell the AI: "If you are about to violate one of my rules, stop and tell me." 6. Conversation You're not just prompting. You're collaborating. "Do not start executing yet. Ask me clarifying questions so we refine the approach step by step." 7. Plan The AI should think before it writes. "List the three rules from my context file that matter most for this task. Then present your execution plan." 8. Alignment Nothing moves forward until both sides agree. "Only begin work once we are aligned." This isn't prompting anymore. This is how you operate with AI.

Unlock the potential of Generative AI to enhance your writing, creativity, and coding skills through prompt engineering. Prompt engineering is a key skill that involves crafting detailed, structured inputs to guide AI towards generating precise, useful outputs. Here are the core strategies to master: - Guide Precisely: Provide detailed instructions for clear, targeted outcomes. - Rich Context: Supply comprehensive background information for more accurate and relevant responses. - Experiment: Start with the basics, then explore more complex requests as you become more comfortable. Improve your AI interactions with these tips: 1. Specificity and Iterations: Craft detailed prompts and refine based on the AI's feedback. 2. Contextual Depth: The more context you provide, the better the AI understands your request, leading to more tailored outputs. 3. Multi-Modal Inputs: Beyond text, incorporate images, code, or data for varied and rich outputs. 4. Example Use: Include examples of what you're aiming for and what you want to avoid to guide the AI more effectively. 5. Advanced Features: Tweak settings like creativity level and response length to get the results you need. 6. Unique Capabilities: Utilize the AI's broad knowledge and support for specific tasks, such as coding assistance. ✍️ Suppose you want to learn a new skill. Here's a prompt template incorporating the above principles: 'I'm eager to learn [Skill Name], aiming to use it for [specific purpose or project]. My background is in [Your Background], and my experience with similar skills is [Your Experience Level]. I aim to build a foundational understanding and complete my first project within [Timeframe]. Could you provide a structured learning path that includes: The key concepts and fundamentals of [Skill Name] I should focus on. Recommendations for online courses, tutorials, and books suitable for beginners. Practical exercises or projects for applying what I learn. Tips for staying motivated and overcoming challenges. Strategies for applying [Skill Name] in real-world situations or job opportunities.' This approach ensures a personalized, goal-oriented learning strategy, leveraging AI's capabilities to support your journey in mastering a new skill. #generativeai #ai #promptengineering #upskill #learning

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?

I often say that in an AI world metacognition is the master capability. This applies at all levels, especially in framing work, but also in interacting with AI. Research reveals specific approaches that yield better outcomes in working with GenAI. Very pleased that Microsoft Research has a significant focus on metacognition, with numerous papers on the topic. One of these, "The Metacognitive Demands and Opportunities of Generative AI", has some particularly instructive findings on both system design and usage: 🧩 Make the task explicit before you prompt. Most prompting interfaces expect you to state clear goals and break work into sub-tasks (e.g., “condense to two paragraphs,” “update the tone”). This metacognitive step is not optional—users who specify goals and decompose tasks gain better control over outputs. 🧠 Treat prompting as a metacognitive exercise. Effective use requires two abilities during iteration: calibrating your confidence (“is it my prompt, parameters, or model randomness?”) and flexibly switching strategies (retry, refine, or decompose further). 🛞 Choose the right interaction mode for control vs. ease. Giving explicit instructions is felt to be harder than inline edits, but it gives more control. Users often struggle at “getting started,” especially when many adjustable parameters are exposed. 🧪 Expect heavier evaluation work when AI generates long content. GenAI outputs (full emails, presentations, or code) shift effort from writing to judging, increasing cognitive load compared to simple auto-complete. People also tend to “eyeball” generated code, risking over-confidence in correctness. ⚡ Watch for fluency-driven overconfidence. Fast, fluent answers can inflate your confidence in both the output and your own evaluation, even when accuracy hasn’t improved. Higher felt confidence then reduces the effort you invest in checking, shortening thinking time and lowering willingness to revise. 🗺️ Use planning aids to improve prompts. Built-in planning support (goal setting + task decomposition) helps users craft better prompts; “prompt chaining” (multi-step sub-tasks) made participants “think through the task better” and target edits more precisely. 🧭🛠️ Reduce demand with explainability and customizability. Surface the right controls (e.g., temperature, shortlist size, output length) and adapt complexity to user state. This can improve self-awareness, confidence, and satisfaction. 🕹️ Support self-evaluation and self-management in the UI. Proactive, neutral nudges based on prior behavior (e.g., “you typically add 15 follow-ups after vague summaries”) can guide users to specify goals up front and reduce rework. ⚖️ Manage cognitive load while improving metacognition. Interventions (decomposition steps, reflections, explanations) add information to process, but studies show metacognitive support can improve outcomes without raising overall load; adapt or fade prompts as skills grow.

Stop asking AI to “brainstorm.” (Do this instead) If you type “Give me 10 creative ideas” into ChatGPT, you will get the average of the internet. You get generic, safe, vanilla patterns. The sea of sameness. To get breakthrough ideas, you need to force the AI off the beaten path using proven creative frameworks. I created this visual guide to replace unstructured requests with 8 specific techniques. Here is the full breakdown to upgrade your next session: 1. Divergent Thinking Focus on volume, not quality. Ask for 20 unique, unconventional ideas without judgment to clear the pipes. 2. Cross-Pollination Take two unrelated concepts and force them together. "Combine the hospitality of a 5-star hotel with the efficiency of a pit crew." 3. Constraint-Based Ideation Creativity loves constraints. "Generate ideas assuming we have only $100 and 24 hours to launch." 4. Role-Playing Scenarios (🌟 My Favorite) This is the most powerful unlock on the list. Pro Tip: Don’t just type this prompt.. use the Voice Mode (Siri-style) in ChatGPT, Gemini, or Claude. Tell the AI: "You are my angriest customer. I'm going to pitch you my new idea, and I want you to tear it apart." Having a literal spoken conversation with a persona surfaces objections and nuances that text prompting often misses. 5. SCAMMPER Technique Don't invent from scratch. Substitute, Combine, Adapt, Modify, Put to another use, Eliminate, or Reverse an existing idea. Modify twice! 6. Mind Mapping Ask the AI to explore the semantic web around your topic to find related sub-themes you haven't considered. 7. “What If” Scenarios Explore the extremes. “What if we had to 100x the value to our customers?" “What if it becomes free?" 8. Visual Brainstorming Switch modalities. Ask for visual concepts, scenes, and imagery descriptions rather than strategic text. Lazy prompts get lazy results. Treat the AI like an expert creative partner that needs direction, not a search engine that needs a keyword. Save this cheat sheet for your next strategy session. ——> Follow along with Matt Savarick to grow 💡 Repost to help your network grow ♻️

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

✨ 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐭𝐚𝐥𝐤𝐬 𝐚𝐛𝐨𝐮𝐭 “𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐛𝐞𝐭𝐭𝐞𝐫 𝐩𝐫𝐨𝐦𝐩𝐭𝐬,” 𝐛𝐮𝐭 𝐫𝐞𝐚𝐥 𝐩𝐫𝐨𝐦𝐩𝐭𝐬 𝐡𝐚𝐯𝐞 𝐚𝐧 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐦𝐨𝐬𝐭 𝐧𝐞𝐯𝐞𝐫 𝐬𝐞𝐞. A good prompt isn’t just a clear sentence, it’s a set of instructions you quietly engineer behind the scenes. Here’s my go-to checklist for prompts that actually deliver: 1. Set the role (Who’s answering?) Are you asking for advice from a career coach or an output from a Python script? Assigning a role instantly upgrades the relevance and depth of the answer. 2. Define the goal (What do you want?) The best prompts spell out what “useful” looks like. Do you want a summary, sample code, a strategic plan, or just raw ideas? Be precise about the win. 3. Add context (What’s the backstory?) Even top models can’t read your mind. Two sentences of context, why you’re asking, what’s happened already, and who’s involved, make the answer 10x smarter. 4. Set constraints (Boundaries, not handcuffs) Short? Formal? Bullet points only? Want to avoid clichés or “as an AI language model” disclaimers? State your non-negotiables up front. 5. Give feedback & iterate The real magic is in versions 2, 3, and 7. Tweak the prompt, rerun it, tighten up until it nails what you need. Don’t settle for the first swing. One common misconception is that better prompts are always longer however, it is not always the case. The best are well-framed, not just wordy. Prompting isn’t about scripting the perfect sentence, it’s about thinking like a designer and building clarity before chasing creativity. What’s one prompt tweak that’s changed your results? #AI #productivity #LLM