How to Improve AI Functionality

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.

🧠 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

Whether you're using Replit Agent, Assistant, or other AI tools, clear communication is key. Effective prompting isn't magic; it's about structure, clarity, and iteration. Here are 10 principles to guide your AI interactions: 🔹 Checkpoint: Build iteratively. Break down large tasks into smaller, testable steps and save progress often. 🔹 Debug: Provide detailed context for errors – error messages, code snippets, and what you've tried. 🔹 Discover: Ask the AI for suggestions on tools, libraries, or approaches. Leverage its knowledge base. 🔹 Experiment: Treat prompting as iterative. Refine your requests based on the AI's responses. 🔹 Instruct: State clear, positive goals. Tell the AI what to do, not just what to avoid. 🔹 Select: Provide focused context. Use file mentions or specific snippets; avoid overwhelming the AI. 🔹 Show: Reduce ambiguity with concrete examples – code samples, desired outputs, data formats, or mockups. 🔹 Simplify: Use clear, direct language. Break down complexity and avoid jargon. 🔹 Specify: Define exact requirements – expected outputs, constraints, data formats, edge cases. 🔹 Test: Plan your structure and features before prompting. Outline requirements like a PM/engineer. By applying these principles, you can significantly improve your collaboration with AI, leading to faster development cycles and better outcomes.

Your engineers don’t need “AI training.” They need to learn how to prompt. We’re building AI agents at Optimal AI, and here’s what’s clear: Prompting is the new interface between humans and machines. If you're serious about building an AI-native engineering team, you need to train them like it’s a muscle — not a magic trick. Here’s what that looks like in practice: 🧱 1. Start with prompt structure. Prompting well is like writing clean function signatures. “You are a senior engineer. Review this PR for security and performance risks. Respond in markdown with line comments and a summary.” 🎯 2. Add tight constraints. AI will try to do everything unless you scope it. “Do not suggest style changes. Focus only on logic bugs and unused code.” 📂 3. Use examples like test cases. The best prompting strategy? Show, don’t just tell. “Here’s a great PR comment. Now generate similar feedback for this diff.” 🧪 4. Prompt like you debug. Engineers already know how to iterate. Prompting is no different. Adjust instructions → rerun → check output → repeat. 🧠 5. Make it part of code review culture. The future dev stack = GitHub + CI + Agents (like Optibot). If your team can't prompt an agent to triage a PR, they’re falling behind. — Your devs don’t need more ChatGPT hacks. They need to think in prompts — like they think in functions, tests, and logs. That’s how you scale engineering productivity with AI.

Are humans 5X better than AI? This paper is blowing up (not in a good way) The recent study claims LLMs are 5x less accurate than humans at summarizing scientific research. That’s a bold claim. But maybe it’s not the model that’s off. Maybe it's the AI strategy, system, prompt, data... What’s your secret sauce for getting the most out of an llm? Scientific summarization is dense, domain-specific, and context-heavy. And evaluating accuracy in this space? That’s not simple either. So just because a general-purpose LLM is struggling with a turing style test... doesn't mean it can't do better. Is it just how they're using it? I think it's short sighted to drop a complex task into an LLM and expect expert results without expert setup. To get better answers, you need a better AI strategy, system, and deployment. Some tips and tricks we find helpful: 1. Start small and be intentional. Don’t just upload a paper and say “summarize this.” Define the structure, tone, and scope you want. Try prompts like: “List three key findings in plain language, and include one real-world implication for each.” The clearer your expectations, the better the output. 2. Test - Build in a feedback loop from the beginning. Ask the model what might be missing from the summary, or how confident it is in the output. Compare responses to expert-written summaries or benchmark examples. If the model can’t handle tasks where the answers are known, it’s not ready for tasks where they’re not. 3. Tweak - Refine everything: prompts, data, logic. Add retrieval grounding so the model pulls from trusted sources instead of guessing. Fine-tune with domain-specific examples to improve accuracy and reduce noise. Experiment with prompt variations and analyze how the answers change. Tuning isn’t just technical. Its iterative alignment between output and expectation. (Spoiler alert: you might be at this stage for a while.) 4. Repeat Every new domain, dataset, or objective requires a fresh approach. LLMs don’t self-correct across contexts, but your workflow can. Build reusable templates. Create consistent evaluation criteria. Track what works, version your changes, and keep refining. Improving LLM performance isn’t one and done. It’s a cycle. Finally: If you treat a language model like a magic button, it's going to kill the rabbit in the hat. If you treat it like a system you deploy, test, tweak, and evolve It can retrieve magic bunnies flying everywhere Q: How are you using LLMs to improve workflows? Have you tried domain-specific data? Would love to hear your approaches in the comments. 

Honestly, most AI developers are still stuck in the last century. It blows my mind how few people are aware of Error Analysis. This is *literally* the fastest and most effective way to evaluate AI applications, and most teams are still stuck chasing ghosts. Please, stop tracking generic metrics and follow these steps: 1. Collect failure samples Start reviewing the responses generated by your application. Write notes about each response, especially those that were mistakes. You don't need to format your notes in any specific way. Focus on describing what went wrong with the response. 2. Categorize your notes After you have reviewed a good set of responses, take an LLM and ask it to find common patterns in your notes. Ask it to classify each note based on these patterns. You'll end up with categories covering every type of mistake your application made. 3. Diagnose the most frequent mistakes Begin by focusing on the most common type of mistake. You don't want to waste time working with rare mistakes. Drill into the conversations, inputs, and logs leading to those incorrect samples. Try to understand what might be causing the problems. 4. Design targeted fixes At this point, you want to determine how to eliminate the mistakes you diagnosed in the previous step as quickly and cheaply as possible. For example, you could tweak your prompts, add extra validation rules, find more training data, or modify the model. 5. Automate the evaluation process You need to implement a simple process to rerun an evaluation set through your application and evaluate whether your fixes were effective. My recommendation is to use an LLM-as-a-Judge to run samples through the application, score them with a PASS/FAIL tag, and compute the results. 6. Keep an eye on your metrics Each category you identified during error analysis is a metric you want to track over time. You will get nowhere by obsessing over "relevance", "correctness", "completeness", "coherence", and any other out-of-the-box metrics. Forget about these and focus on the real issues you found.

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

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.

Just published: "The Prompt Engineering Playbook for Programmers" My latest free write-up: https://lnkd.in/g9Kxa7hG ✍️ After working with AI coding assistants daily, I've learned that the quality of your output depends entirely on the quality of your prompts. A vague "fix my code" gets you generic advice, while a well-crafted prompt can produce thoughtful, accurate solutions. I've distilled the key patterns and frameworks that actually work into a playbook covering: ✅ Patterns that work - Role-playing, rich context, specific goals, and iterative refinement ✅ Debugging strategies - From "my code doesn't work" to surgical problem-solving ✅ Refactoring techniques - How to get AI to improve performance, readability, and maintainability ✅ Feature implementation - Building new functionality step-by-step with AI as your pair programmer ✅ Common anti-patterns - What NOT to do (and how to fix it when things go wrong) The article includes side-by-side comparisons of poor vs. improved prompts with actual AI responses, plus commentary on why one succeeds where the other fails. Key insight: Treat AI coding assistants like very literal, knowledgeable collaborators. The more context you provide, the better the output. It's not magic - it's about communication. Whether you're debugging a tricky React hook, refactoring legacy code, or implementing a new feature, these techniques can turn AI from a frustrating tool into a true development partner. #ai #softwareengineering #programming

Researchers have unveiled a self-harmonized Chain-of-Thought (CoT) prompting method that significantly improves LLMs’ reasoning capabilities. This method is called ECHO. ECHO introduces an adaptive and iterative refinement process that dynamically enhances reasoning chains. It starts by clustering questions based on semantic similarity, selecting a representative question from each group, and generating a reasoning chain using zero-shot CoT prompting. The real magic happens in the iterative process: one chain is regenerated at random while others are used as examples to guide the improvement. This cross-pollination of reasoning patterns helps fill gaps and eliminate errors over multiple iterations. Compared to existing baselines like Auto-CoT, this new approach yields a +2.8% performance boost in arithmetic, commonsense, and symbolic reasoning tasks. It refines reasoning by harmonizing diverse demonstrations into consistent, accurate patterns and continuously fine-tunes them to improve coherence and effectiveness. For AI engineers working at an enterprise, implementing ECHO can enhance the performance of your LLM-powered applications. Start by training your model to identify clusters of similar questions or tasks in your specific domain. Then, implement zero-shot CoT prompting for each representative task, and leverage ECHO’s iterative refinement technique to continually improve accuracy and reduce errors. This innovation paves the way for more reliable and efficient LLM reasoning frameworks, reducing the need for manual intervention. Could this be the future of automatic reasoning in AI systems? Paper https://lnkd.in/gAKJ9at4 — Join thousands of world-class researchers and engineers from Google, Stanford, OpenAI, and Meta staying ahead on AI http://aitidbits.ai