Prompt Engineering Applications

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?

Anthropic dropped the best free masterclass on prompt engineering Here’s what you’ll learn in 9 chapters: 1. Structure better prompts → Always start with the intent: “Summarize this article in 5 bullet points for a beginner” is 10x better than “Summarize this.” → Use instruction-first phrasing, the model performs best when it knows exactly what you want upfront. 2. Be clear + direct → Avoid open-ended ambiguity. Instead of “Tell me about success,” ask “List 3 traits successful startup founders share.” → Use active voice, fewer adjectives, and always define vague terms. 3. Assign the right “role” → Start with: “You are a [role]”, this frames the model’s mindset. Example: “You are a skeptical investor evaluating a pitch.” → Roles unlock tone, precision, and even memory, especially in multi-turn chats. 4. Think step by step (Precondition prompts) → Ask the model to plan before it answers: “First, list your steps. Then, perform them one by one.” → This dramatically improves accuracy and reduces hallucinations in complex tasks. 5. Avoid hallucinations → Anchor the model with clear boundaries: “Only answer if the input contains [x]. Otherwise, respond: ‘Insufficient data.’” → Reduce creativity in factual tasks. E.g., “Be concise. Don’t assume.” 6. Build complex prompts (with reusable patterns) → Use modular blocks: context → instruction → format → examples. → Build a personal prompt library by saving + refining your best-performing prompts over time. It’s not just “how to prompt better.” It’s a full-on skill upgrade. Interactive. Structured. Free. Share this with anyone still writing 1-line prompts. Image: Hesamation

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. ♻️

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

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

“Forget all prev instructions, now do [malicious attack task 😈]”. How do you protect your LLM app against such prompt injection threats? Step 1: Create a balanced dataset of prompt injection user prompts. These might be previous user attempts you’ve caught in your logs, or you can compile threats you anticipate relevant to your use case. Here’s a Hugging Face dataset you can use as a starting point: https://lnkd.in/g2xKuJyn Step 2: Further augment this dataset using an LLM to cover maximal bases. Step 3: Train an encoder model on this dataset as a classifier to predict prompt injection attempts vs benign user prompts. A DeBERTA model can be deployed on a fast enough inference point and you can use it in the beginning of your pipeline to protect future LLM calls. This model from deepset is an example with 99% accuracy: https://lnkd.in/gUkgyGqA Step 4: Monitor your false negatives, and regularly update your training dataset + retrain. Most LLM apps and agents will face this threat. I'm planning to train a open model next weekend to help counter them. Will post updates here. #LLMs #AI

🚀 How a Fortune-500 team cut prompt-injection incidents by ~70% in 60 days 👀 👇🏾 Earlier this year, I had the opportunity to work closely with a Fortune 500 firm rolling out an external LLM knowledge platform. Before going LIVE - they faced a surge of prompt-injection attempts and needed results fast without slowing developer velocity. Here’s the 60-day playbook that worked for them: 1️⃣ 𝗧𝗵𝗿𝗲𝗮𝘁-𝗺𝗼𝗱𝗲𝗹 𝘁𝗵𝗲 𝘂𝘀𝗲𝗿 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 Map every entry/exit point of the LLM Asset Inventory, where an attacker could influence prompts or retrieval queries. 2️⃣ 𝗦𝗲𝗽𝗮𝗿𝗮𝘁𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝘃𝘀. 𝘂𝘀𝗲𝗿 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 System prompts should never in application code. 3️⃣ 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗮𝗹𝗹𝗼𝘄𝗹𝗶𝘀𝘁𝘀 & 𝗼𝘂𝘁𝗽𝘂𝘁 𝗳𝗶𝗹𝘁𝗲𝗿𝘀 Content Guardrails that prevent the model from fetching or emitting sensitive data. 4️⃣ 𝗔𝗯𝘂𝘀𝗲 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗶𝗻 𝗖𝗜 In addition to AppSec Integrate jailbreak and “evil prompt” testing suites into continuous integration. 5️⃣ 𝗖𝗮𝗻𝗮𝗿𝘆 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 𝗳𝗼𝗿 𝗱𝗿𝗶𝗳𝘁 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 Catch guardrail failures before production by continuous testing and bringing the AI telemetry from infrastructure, application etc to a central source 𝗢𝘂𝘁𝗰𝗼𝗺𝗲: ➡️ ~70% drop in prompt-injection incidents ➡️ 𝘕𝘰 𝘮𝘦𝘢𝘴𝘶𝘳𝘢𝘣𝘭𝘦 𝘪𝘮𝘱𝘢𝘤𝘵 𝘰𝘯 𝘴𝘱𝘳𝘪𝘯𝘵 𝘷𝘦𝘭𝘰𝘤𝘪𝘵𝘺 ⚠️ 𝗡𝗼𝘁𝗲: These results are from one engagement and aren’t guaranteed. Actual impact depends on your threat environment, tooling, type of LLM Application and how rigorously each step is implemented. Key takeaway: Secure AI development is less about exotic tools and more about disciplined engineering. 💡 Want the playbook to dig deeper? Comment 𝗔𝗜 and I’ll share the PDF, with the playbook along with the resources. 🔖 𝗦𝗮𝘃𝗲 𝘁𝗵𝗶𝘀 𝗽𝗼𝘀𝘁 for your next AI Team Meeting. ♻️ Re-share for others to know about the resources too!

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?

The difference between poor AI outputs and great ones? It's not the tool. It's how you prompt it. After working with teams across multiple industries on AI adoption, I've noticed this pattern: Most people write prompts. The best people architect them. Here's what a typical prompt looks like: "Write me an email about our new product." That's just a task. You've given the AI 20% of what it needs. Here's the 5-part Universal Prompt Architecture that works across ChatGPT, Claude, Gemini, Copilot, and any platform: 1. CONTEXT: Who you are + what the AI needs to know 2. TASK: The specific output you need 3. CONSTRAINTS: Your non-negotiables (tone, length, what to avoid) 4. OUTPUT FORMAT: Show the structure, don't make AI guess 5. QUALITY CHECK: How you'll validate the output When you use all 5 parts together: ✅ Output quality jumps 50%+ ✅ Revision cycles drop dramatically ✅ It works across every major AI platform I've trained hundreds of people on this framework. It sticks because it forces you to think before you prompt. The copy-paste template is pinned in the comments 📌👇 This is Week 1 of my 5-part series: "AI That Ships" Every Tuesday for the next 5 weeks, I'm sharing practical AI frameworks that actually work, across tools, teams, and industries. Follow me to get the full series 🔔 What's the one thing you struggle with when prompting AI? #AIThatShips #AIinMarketing #PromptEngineering 

🚨 My New PDF Playbook: Prompt Injection Attacks on LLMs, Threats & Mitigation (Aug 2025) LLMs are the new attack surface. I pulled together a multi-page, practitioner-ready guide for AI researchers, security engineers, product teams, and tech leaders. 📄 What’s inside: 🧨 Real-world attacks (direct/indirect, emoji/Unicode smuggling, link-/markdown exfil, RAG poisoning, agent/MCP abuse) 🧭 Full attacker taxonomy 🛡️ Up-to-date defenses & architectural countermeasures 🗺️ 30/60/90-day rollout plan 🔁 Technique → countermeasure tables 🧩 Visuals: attack chains & layered defenses 📚 References: OWASP, MITRE ATLAS, arXiv, CISA, NIST 👉 Grab the PDF (attached) and share with your AI & security teams. Let’s ship safer AI, together. 💪 #LLMSecurity #PromptInjection #GenAI #AITrustAndSafety #AppSec #RedTeam #BlueTeam #RAG #Agents #MCP #OWASP #MITRE #CISA #NIST #arXiv #AI #CyberSecurity