How to Improve AI Output Through Iteration

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.

How I'm Using a "Data Feedback Cycle" to Improve My AI Outputs. Most initial AI outputs suck. Common flow... - you type in a generic prompt - you get back a watered-down output - you get frustrated - you conclude AI isn't "good" at this problem - you go back to the old way But if you feed the model better data, you'll get a better output (duh.) Note, "data" can be contextual or hard data (events, transactions, customer feedback, financials). 1. The Data <> Feedback Cycle This is basically how I work through a problem. Btw, I mainly use Claude, but do use ChatGPT/Perplexity depending on case. - I think about the problem - I feed in some data/context that might be useful - I get an output that's partly useful - I identify data/context that could improve the output* - I collect and re-load the new data - I re-run the prompt - I get a better output Wash, rinse, repeat until satisfied. *Bonus tip, you can ask the AI "what data or context would help you give a better output?" 2. Real Example: Ecom/DTC Product Conversion Let's say you're an ecom company struggling with product conversion rates. You start by asking an AI to analyze why people aren't converting from your PDPs. The initial output is generic: - improving product descriptions - adding more photos - social proof - simplifying the checkout process - blah, blah, meh. Not very helpful. So we give the model more data. - context about your brand/industry/competitors - context on your audience and ICP - product-level conversion data (view, add, purchase) - product details and taxonomies (category, type, attributes) - customer return reasons - unstructured product review data - upload screenshots/wires of your pages Now you should get a much more specific and actionable output. - 52% of product returns for denim are related to sizing - Specifically the length and inseam - Consider improving size charts and size information 3. Why This Process Is So Much Faster The traditional approach can take weeks/months. - Boss asks how to improve conversion - You read a bunch of articles/books (hours) - You realize you need more data (weeks) - Set up ways to collect it (weeks) - Wait for enough data (months) - Analyze the data by hand (weeks) - Present findings (days) - Fix the problem (weeks) The Data <> AI cycle is much faster. - AI summarizes best practices (minutes) - AI shows what data you're missing (minutes) - Add new tracking (days) - Collect just enough data (days/weeks) - AI analyzes data and patterns (minutes) This makes everything much faster. So if you've tried AI w/ mediocre results, try feeding it better data. Even if it takes you a couple of hours of iterating, it's still MUCH faster than the old way. Which data sources have you found most valuable to feed into AI tools? Anything surprising that made a big difference in the output quality? #ai #data #context

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.

Prompting isn’t the hard part anymore. Trusting the output is. You finally get a model to reason step-by-step… And then? You're staring at a polished paragraph, wondering:    > “Is this actually right?”    > “Could this go to leadership?”    > “Can I trust this across markets or functions?” It looks confident. It sounds strategic. But you know better than to mistake that for true intelligence. 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗿𝗶𝘀𝗸: Most teams are experimenting with AI. But few are auditing it. They’re pushing outputs into decks, workflows, and decisions— With zero QA and no accountability layer 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝘁𝗲𝗹𝗹 𝗽𝗲𝗼𝗽𝗹𝗲: Don’t just validate the answers. Validate the reasoning. And that means building a lightweight, repeatable system that fits real-world workflows. 𝗨𝘀𝗲 𝘁𝗵𝗲 𝗥.𝗜.𝗩. 𝗟𝗼𝗼𝗽: 𝗥𝗲𝘃𝗶𝗲𝘄 – What’s missing, vague, or risky? 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 – Adjust one thing (tone, data, structure). 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 – Rerun and compare — does this version hit the mark? Run it 2–3 times. The best version usually shows up in round two or three, not round one.  𝗥𝘂𝗻 𝗮 60-𝗦𝗲𝗰𝗼𝗻𝗱 𝗢𝘂𝘁𝗽𝘂𝘁 𝗤𝗔 𝗕𝗲𝗳𝗼𝗿𝗲 𝗬𝗼𝘂 𝗛𝗶𝘁 𝗦𝗲𝗻𝗱: • Is the logic sound? • Are key facts verifiable? • Is the tone aligned with the audience and region? • Could this go public without risk? 𝗜𝗳 𝘆𝗼𝘂 𝗰𝗮𝗻’𝘁 𝘀𝗮𝘆 𝘆𝗲𝘀 𝘁𝗼 𝗮𝗹𝗹 𝗳𝗼𝘂𝗿, 𝗶𝘁’𝘀 𝗻𝗼𝘁 𝗿𝗲𝗮𝗱𝘆. 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗜𝗻𝘀𝗶𝗴𝗵𝘁: Prompts are just the beginning. But 𝗽𝗿𝗼𝗺𝗽𝘁 𝗮𝘂𝗱𝗶𝘁𝗶𝗻𝗴 is what separates smart teams from strategic ones. You don’t need AI that moves fast. You need AI that moves smart. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗿𝘂𝘀𝘁 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗼𝘂𝘁𝗽𝘂𝘁𝘀? 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 for weekly playbooks on leading AI-powered teams. 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 to my newsletter for systems you can apply Monday morning, not someday.

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!