Engineering

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.

𝐀𝐈 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐩𝐫𝐨𝐦𝐩𝐭 𝐦𝐞𝐭𝐫𝐢𝐜𝐬 𝐢𝐬 𝐥𝐢𝐤𝐞 𝐬𝐚𝐥𝐞𝐬 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐫𝐚𝐭𝐞𝐬. 𝘛𝘩𝘦 𝘍𝘶𝘵𝘶𝘳𝘦 𝘰𝘧 𝘈𝘐 𝘈𝘨𝘦𝘯𝘵𝘴: 𝘔𝘦𝘢𝘴𝘶𝘳𝘪𝘯𝘨 𝘗𝘳𝘰𝘮𝘱𝘵 𝘚𝘶𝘤𝘤𝘦𝘴𝘴 𝘸𝘪𝘵𝘩 𝘗𝘳𝘦𝘤𝘪𝘴𝘪𝘰𝘯 Most AI agents fail not from bad models but from weak prompts. Advanced 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 isn’t just about crafting inputs. It’s about 𝐦𝐞𝐚𝐬𝐮𝐫𝐢𝐧𝐠 impact. How do we assess prompt success? 𝐁𝐞𝐲𝐨𝐧𝐝 𝐠𝐮𝐭 𝐟𝐞𝐞𝐥𝐢𝐧𝐠. 𝐁𝐞𝐲𝐨𝐧𝐝 𝐠𝐮𝐞𝐬𝐬𝐰𝐨𝐫𝐤. 𝐇𝐨𝐰 𝐭𝐨 𝐂𝐫𝐞𝐚𝐭𝐞 𝐏𝐫𝐨𝐦𝐩𝐭 𝐀𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭 𝐌𝐞𝐭𝐫𝐢𝐜𝐬: 1) 𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐜𝐞 𝐒𝐜𝐨𝐫𝐞: Are outputs aligned with intent? 2) 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 & 𝐑𝐞𝐜𝐚𝐥𝐥:  Does the AI retrieve the right information? 3) 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐞 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲:  Are outputs concise and useful? 4) 𝐔𝐬𝐞𝐫 𝐒𝐚𝐭𝐢𝐬𝐟𝐚𝐜𝐭𝐢𝐨𝐧: Do users trust and use the response? 5) 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐈𝐦𝐩𝐚𝐜𝐭: Does it drive action in sales or engagement? 6) 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲: Does it improve efficiency in manufacturing workflows? 7) 𝐓𝐡𝐫𝐞𝐚𝐭 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐑𝐚𝐭𝐞: Does it enhance security without false alarms? 8) 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐲 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: Does the AI make reliable and context-aware decisions? 𝑪𝒂𝒔𝒆 𝑺𝒕𝒖𝒅𝒊𝒆𝒔: ↳ 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐮𝐩𝐩𝐨𝐫𝐭: AI reduced resolution time by 40% through clearer prompts. ↳ 𝐋𝐞𝐠𝐚𝐥 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡: AI cut irrelevant results by 60% by optimizing specificity. ↳ 𝐒𝐚𝐥𝐞𝐬 𝐎𝐮𝐭𝐫𝐞𝐚𝐜𝐡:  AI boosted reply rates by 35% with refined personalization. ↳ 𝐄-𝐜𝐨𝐦𝐦𝐞𝐫𝐜𝐞 𝐒𝐞𝐚𝐫𝐜𝐡: AI improved product matches by 50% with structured prompts. ↳ 𝐌𝐞𝐝𝐢𝐜𝐚𝐥 𝐀𝐈: AI reduced diagnostic errors by 30% by improving context clarity. ↳ 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐀𝐈: AI improved defect detection by 45% by enhancing prompt precision. ↳ 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐀𝐈: AI reduced false alerts by 50% in fraud detection systems. ↳ 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐀𝐈: AI enhanced robotics decision-making by 55%, reducing human intervention. 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝐦𝐚𝐭𝐭𝐞𝐫. Precision beats intuition. AI Agents thrive when we measure what works. What’s your framework for 𝐏𝐫𝐨𝐦𝐩𝐭 𝐀𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬? ♻️ Repost to your LinkedIn followers if AI should be more accessible and follow Timothy Goebel for expert insights on AI & innovation. #AIagents #PromptEngineering #AIMetrics #ArtificialIntelligence #TechInnovation

You’re doing it. I’m doing it. Your friends are doing it. Even the leaders who deny it are doing it. Everyone’s experimenting with AI. But I keep hearing the same complaint: “It’s not as game-changing as I thought.” If AI is so powerful, why isn’t it doing more of your work? The #1 obstacle keeping you and your team from getting more out of AI? You're not bossing it around enough. AI doesn’t get tired and it doesn't push back. It doesn’t give you a side-eye when at 11:45 pm you demand seven rewrite options to compare while snacking in your bathrobe. Yet most people give it maybe one round of feedback—then complain it’s “meh.” The best AI users? They iterate. They refine. They make AI work for them. Here’s how: 1. Tweak AI's basic setting so it sounds like you AI-generated text can feel robotic or too formal. Fix that by teaching it your style from the start. Prompt: “Analyze the writing style below—tone, sentence structure, and word choice—and use it for all future responses.” (Paste a few of your own posts or emails.) Then, take the response and add it to Settings → Personalization → Custom Instructions. 2. Strip Out the Jargon Don’t let AI spew corporate-speak. Prompt: “Rewrite this so a smart high schooler could understand it—no buzzwords, no filler, just clear, compelling language.” or “Use human, ultra-clear language that’s straightforward and passes an AI detection test.” 3. Give It a Solid Outline AI thrives on structure. Instead of “Write me a whitepaper,” start with bullet points or a rough outline. Prompt: “Here’s my outline. Turn it into a first draft with strong examples, a compelling narrative, and clear takeaways.” Even better? Record yourself explaining your idea; paste the transcript so AI can capture your authentic voice. 4. Be Brutally Honest If the output feels off, don’t sugarcoat it. Prompt: “You’re too cheesy. Make this sound like a Fortune 500 executive wrote it.” or “Identify all weak, repetitive, or unclear text in this post and suggest stronger alternatives.” 5. Give it a tough crowd Polished isn’t enough—sometimes you need pushback. Prompt: “Pretend you’re a skeptical CFO who thinks this idea is a waste of money. Rewrite it to persuade them.” or “Act as a no-nonsense VC who doesn’t buy this pitch. Ask 5 hard questions that make me rethink my strategy.” 6. Flip the Script—AI Interviews You Sometimes the best answers come from sharper questions. Prompt: “You’re a seasoned journalist interviewing me on this topic. Ask thoughtful follow-ups to surface my best thinking.” This back-and-forth helps refine your ideas before you even start writing. The Bottom Line: AI isn’t the bottleneck—we are. If you don’t push it, you’ll keep getting mediocrity. But if you treat AI like a tireless assistant that thrives on feedback? You’ll unlock content and insights that truly move the needle. Once you work this way, there’s no going back.

The ability to effectively communicate with generative AI tools has become a critical skill. A. Here's some tips on getting the best results: 1) Be crystal clear - Replace "Tell me about oceans" with "Provide an overview of the major oceans and their unique characteristics" 2) Provide context - Include relevant background information and constraints Structure logically - Organize instructions, examples, and questions in a coherent flow. 3) Stay concise - Include only the necessary details. B. Try the "Four Pillars:" 1) Task - Use specific action words (create, analyze, summarize) 2) Format - Specify desired output structure (list, essay, table) 3) Voice - Indicate tone and style (formal, persuasive, educational) 4) Context - Supply relevant background and criteria C. Advanced Techniques: 1) Chain-of-Thought Prompting - Guide AI through step-by-step reasoning. 2) Assign a Persona - "Act as an expert historian" to tailor expertise level. 3) Few-Shot Prompting - Provide examples of desired outputs. 4) Self-Refine Prompting - Ask AI to critique and improve its own responses. D. Avoid: 1) Vague instructions leading to generic responses. 2) Overloading with too much information at once. What prompting techniques have yielded the best results in your experience? #legaltech #innovation #law #business #learning

Prompt engineering remains one the most effective alignment strategies because it allows developers to steer LLM behavior without modifying model weights, enabling fast, low-cost iteration. It also leverages the model’s pretrained knowledge and internal reasoning patterns, making alignment more controllable and interpretable through natural language instructions. However, it doesn’t come without cons, such as fragility of prompts (ex: changing one word can lead to different behavior), and scalability limits (ex: prompt engineer limits long chain reasoning capabilities). However, different tasks demand different prompting strategies, allowing you to select what best fit your business objectives, including budget constraints. If you're building with LLMs, you need to know when and how to use these. Let’s break them down: 1.🔸Chain of Thought (CoT) Teach the AI to solve problems step-by-step by breaking them into logical parts for better reasoning and clearer answers. 2.🔸ReAct (Reason + Act) Alternate between thinking and doing. The AI reasons, takes action, evaluates, and then adjusts based on real-time feedback. 3.🔸Tree of Thought (ToT) Explore multiple reasoning paths before selecting the best one. Helps when the task has more than one possible approach. 4.🔸Divide and Conquer (DnC) Split big problems into subtasks, handle them in parallel, and combine the results into a comprehensive final answer. 5.🔸Self-Consistency Prompting Ask the AI to respond multiple times, then choose the most consistent or commonly repeated answer for higher reliability. 6.🔸Role Prompting Assign the AI a specific persona like a lawyer or doctor to shape tone, knowledge, and context of its replies. 7.🔸Few-Shot Prompting Provide a few good examples and the AI will pick up the pattern. Best for structured tasks or behavior cloning. 8.🔸Zero-Shot Chain of Thought Prompt the AI to “think step-by-step” without giving any examples. Great for on-the-fly reasoning tasks. Was this type of guide useful to you? Let me know below. Follow for plug-and-play visuals, cheat sheets, and step-by-step agent-building guides. #genai #promptengineering #artificialintelligence

Some of the best AI breakthroughs we’ve seen came from small, focused teams working hands-on, with structured inputs and the right prompting. Here’s how we help clients unlock AI value in days, not months: 1. Start with a small, cross-functional team (4–8 people) 1–2 subject matter experts (e.g., supply chain, claims, marketing ops) 1–2 technical leads (e.g., SWE, data scientist, architect) 1 facilitator to guide, capture, and translate ideas Optional: an AI strategist or business sponsor 2. Context before prompting - Capture SME and tech lead deep dives (recorded and transcribed) - Pull in recent internal reports, KPIs, dashboards, and documentation - Enrich with external context using Deep Research tools: Use OpenAI’s Deep Research (ChatGPT Pro) to scan for relevant AI use cases, competitor moves, innovation trends, and regulatory updates. Summarize into structured bullets that can prime your AI. This is context engineering: assembling high-signal input before prompting. 3. Prompt strategically, not just creatively Prompts that work well in this format: - “Based on this context [paste or refer to doc], generate 100 AI use cases tailored to [company/industry/problem].” - “Score each idea by ROI, implementation time, required team size, and impact breadth.” - “Cluster the ideas into strategic themes (e.g., cost savings, customer experience, risk reduction).” - “Give a 5-step execution plan for the top 5. What’s missing from these plans?” - “Now 10x the ambition: what would a moonshot version of each idea look like?” Bonus tip: Prompt like a strategist (not just a user) Start with a scrappy idea, then ask AI to structure it: - “Rewrite the following as a detailed, high-quality prompt with role, inputs, structure, and output format... I want ideas to improve our supplier onboarding process with AI. Prioritize fast wins.” AI returns something like: “You are an enterprise AI strategist. Based on our internal context [insert], generate 50 AI-driven improvements for supplier onboarding. Prioritize for speed to deploy, measurable ROI, and ease of integration. Present as a ranked table with 3-line summaries, scoring by [criteria].” Now tune that prompt; add industry nuances, internal systems, customer data, or constraints. 4. Real examples we’ve seen work: - Logistics: AI predicts port congestion and auto-adjusts shipping routes - Retail: Forecasting model helps merchandisers optimize promo mix by store cluster 5. Use tools built for context-aware prompting - Use Custom GPTs or Claude’s file-upload capability - Store transcripts and research in Notion, Airtable, or similar - Build lightweight RAG pipelines (if technical support is available) - Small teams. Deep context. Structured prompting. Fast outcomes. This layered technique has been tested by some of the best in the field, including a few sharp voices worth following, including Allie K. Miller!

Which is it: use LLMs to improve the prompt, or is that over-engineering? By now, we've all seen a 1000 conflicting prompt guides. So, I wanted to get back to the research: • What do actual studies say? • What actually works in 2025 vs 2024? • What do experts at OpenAI, Anthropic, & Google  say? I spent the past month in Google Scholar, figuring it out. I firmed up the learnings with Miqdad Jaffer at OpenAI. And I'm ready to present: "The Ultimate Guide to Prompt Engineering in 2025: The Latest Best Practices." https://lnkd.in/d_qYCBT7 We cover: 1. Do You Really Need Prompt Engineering? 2. The Hidden Economics of Prompt Engineering 3. What the Research Says About Good Prompts 4. The 6-Layer Bottom-Line Framework 5. Step-by-step: Improving Your Prompts as a PM 6. The 301 Advanced Techniques Nobody Talks About 7. The Ultimate Prompt Template 2.0 8. The 3 Most Common Mistakes Some of my favorite takeaways from the research: 1. It's not just revenue, but cost You have to realize that APIs charge by number of input and output tokens. An engineered prompt can deliver the same quality with 76% cost reduction. We're talking $3,000 daily vs $706 daily for 100k calls. 2. Chain-of-Table beats everything else This new technique gets 8.69% improvement on structured data by manipulating table structure step-by-step instead of reasoning about tables in text. For things like financial dashboards and data analysis tools, it's the best. 3. Few-shot prompting hurts advanced models OpenAI's o1 and DeepSeek's R1 actually perform worse with examples. These reasoning models don't need your sample outputs - they're smart enough to figure it out themselves. 4. XML tags boost Claude performance Anthropic specifically trained Claude to recognize XML structure. You get 15-20% better performance just by changing your formatting from plain text to XML tags. 5. Automated prompt engineering destroys manual AI systems create better prompts in 10 minutes than human experts do after 20 hours of careful optimization work. The machines are better at optimizing themselves than we are. 6. Most prompting advice is complete bullshit Researchers analyzed 1,500+ academic papers and found massive gaps between what people claim works and what's actually been tested scientifically. And what about Ian Nuttal's tweet? Well, Ian's right about over-engineering. But for products, prompt engineering IS the product. Bolt hit $50M ARR via systematic prompt engineering. The Key? Knowing when to engineer vs keep it simple.

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

🧠 Designing AI That Thinks: Mastering Agentic Prompting for Smarter Results Have you ever used an LLM and felt it gave up too soon? Or worse, guessed its way through a task? Yeah, I've been there. Most of the time, the prompt is the problem. To get AI that acts more like a helpful agent and less like a chatbot on autopilot, you need to prompt it like one. Here are the three key components of an effective 🔁 Persistence: Ensure the model understands it's in a multi-turn interaction and shouldn't yield control prematurely. 🧾 Example: "You are an agent; please continue working until the user's query is resolved. Only terminate your turn when you are certain the problem is solved." 🧰 Tool Usage: Encourage the model to use available tools, especially when uncertain, instead of guessing. 🧾 Example:" If you're unsure about file content or codebase structure related to the user's request, use your tools to read files and gather the necessary information. Do not guess or fabricate answers." 🧠 Planning: Prompt it to plan before actions and reflect afterward. Prevent reactive tool calls with no strategy. 🧾 Example: "You must plan extensively before each function call and reflect on the outcomes of previous calls. Avoid completing the task solely through a sequence of function calls, as this can hinder insightful problem-solving." 💡 I've used this format in AI-powered research and decision-support tools and saw a clear boost in response quality and reliability. 👉 Takeaway: Agentic prompting turns a passive assistant into an active problem solver. The difference is in the details. Are you using these techniques in your prompts? I would love to hear what's working for you; leave a comment, or let's connect! #PromptEngineering #AgenticPrompting #LLM #AIWorkflow

Stuck on your current AI model because switching feels like too much work? We just shipped automated prompt optimization in Freeplay to solve exactly this problem. We see the following patterns constantly: Teams do weeks of prompt engineering to make GPT-4o work well for their use case. Then Gemini 2.5 Flash comes out with the promise of better performance or cost, but nobody wants to re-optimize all their prompts from scratch. So they stay stuck on the old model, even when better options exist. Or: A PM see the same set of recurring problems with production prompts and wants to try out some changes, but doesn't feel confident about all the latest prompt engineering best practices. It can feel like a never-ending set of tweaks trying to make things incrementally better, but is it worth it? And could it happen faster? ✨ A better approach: Use your production data to automate prompt engineering. We've been experimenting with more and more uses of AI in Freeplay, and this one consistently works: 1. Decide which prompt you want to optimize and which model you want to optimize for. Write some short instructions if you'd like about what you want to change. 2. Use production data including logs with auto-eval scores, customer feedback, and human labels from your team as inputs to automatically generate optimized prompts with Freeplay's agent. 3. Instantly launch a test with your preferred dataset and your custom eval criteria to see how the new, optimized prompt & model combo compares to your old one. Compare any prompt version and model head-to-head (Claude Sonnet 4 vs Opus 4.1, GPT vs Gemini, etc.). 4. Get detailed explanations of every change and view side-by-side diffs for further validation. All the changes are fully transparent, and you can keep iterating by hand as you'd like. Instead of manual hours analyzing logs and running experiments, your production evaluation results, customer feedback, and human annotations become fuel for continuous optimization. How it works: Click "Optimize" on any prompt → Our agent analyzes your production data → Get an optimized version with diff view → Auto-run your evals to validate improvements More like this coming soon! The future of AI product development will be increasingly automated optimization workflows, where agents help evaluate and improve other agents. Try it now if you're a Freeplay customer - just click "Optimize" on any prompt. #AIProductDevelopment #PromptEngineering #ProductStrategy #AutomatedOptimization #LLMs