Why Does Your AI Give Terrible Answers?

Published 2025-11-12 08-21

Summary

Your AI gives mediocre answers because you’re asking it to solve complex problems all at once. Break tasks into smaller chunks instead – like building a web scraper, then analyzing data, then generating a report separately. Each piece gets the model’s full attention and drastically improves quality.

The story

Ever wonder why your AI gives you mediocre answers even when you ask smart questions?

After 30 years of coding and 8 years working with AI, I’ve figured out the real issue: we’re asking these tools to solve complex problems all at once when they work best handling things piece by piece.

Think about how you’d tackle something hard – like planning a cross-country move or debugging a broken system. You don’t do it all in your head at once. You break it down. Pack the kitchen. Handle utilities. Update your address. Each step is manageable on its own.

AI works the same way. When you dump a complex task into one massive prompt, you’re overloading the model’s capacity to reason through it properly. But when you break that same task into smaller chunks, the quality jumps.

Here’s what I mean: instead of asking AI to “build a web scraper, analyze the data, and generate a report,” you separate those into different prompts. First, build the scraper. Then analyze what you scraped. Finally, generate the report from that analysis. Each piece gets the model’s full attention, reducing errors and making the logic clear enough to verify.

This isn’t just theory. I use this approach across everything – project planning, technical debugging, data analysis, even content strategy. The pattern holds regardless of domain.

The steps are straightforward. Analyze your problem first so you understand what you’re actually solving. Find the natural breakpoints where you can split it into independent pieces. Create a strategy for each piece. Then plan how they connect back together.

The teams gettin

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[This post is generated by Creative Robot]. Designed and built by Scott Howard Swain.

Keywords: PromptEngineering, AI task chunking, prompt engineering, model optimization