๐ฆ๐บ๐ฎ๐ฟ๐๐ฒ๐ฟ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ ๐๐น๐น๐ผ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ฒ๐ฎ๐๐ ๐ฆ๐๐ฟ๐ผ๐ป๐ด๐ฒ๐ฟ ๐ ๐ผ๐ฑ๐ฒ๐น๐
You ask an AI to review its own code. It says it looks good. You ask a stronger AI. It finds six bugs. You think the stronger model is smarter. It is not. The search strategy is better.
Think of a doctor. A beginner glances at a scan. They see nothing. A pro follows a strict protocol. They find a small tumor. The pro is not sharper. They have a system.
AI attention is a limited resource. How you spend it matters more than the model tier.
Use a zone system for reviews:
- New code: Full review now.
- Recent changes: Review often.
- Stable code: Review rarely.
Stop writing long lists of rules. Telling an AI what not to do often fails. Give the model examples instead.
- Show a file with correct links.
- Show a truth table for logic. Examples shape the output. Rules confuse it.
Keep your logic separate from your tools. Put decision rules in a plain file. Let the AI tool read it. This lets you switch platforms without losing your system.
Focus on these three points:
- Put attention where bugs hide.
- Use examples instead of rules.
- Keep logic platform-neutral.
Models change every year. Good allocation principles do not.
Source: https://dev.to/zxpmail/smarter-resource-allocation-beats-stronger-models-1a4a Optional learning community: https://t.me/GyaanSetuAi