AI tools fail for one simple reason: they lose track of context.

That’s been the trick all along.

When the context window loses sight of what you want—what you're trying to do, what the constraints are, and what info you're working with—it will take its best guess and likely do things wrong.

That's where RAG came into the spotlight—Retrieval Augmented Generation. It works by pulling in relevant documents—like your project specs, recent code changes, or user instructions—and feeding them into the AI before it responds.

Modern coding tools make this easier. You can quickly point them to relevant files—rules, examples, or references—and they’ll use that to reason better.

Now, some tools even do this part themselves. They scan the codebase and build a plan. First step: get the right context.

If they miss, it's your job to guide them back on track. These tools depend entirely on the information they’re given.

Without the right context, they spin. With it, they fly.