Tools: I Made AI Study My Codebase Before Writing A Single Line 2026
Posted on Feb 14
• Originally published at itnext.io
Adding "because" to corrections helps AI apply principles within a session. But sessions end. Tomorrow, AI starts fresh.
What if AI already knew your project's patterns at the start of every session?
That's what this article is about: building context that persists.
Before you write any instructions manually, let AI do the initial work.
An example prompt I might use for illustration purpose:
Read through this codebase. What patterns do you see that I'd probably correct you on if you got them wrong? Focus on:
AI scans your code and surfaces patterns. Not everything it finds will be right. But it gives you a starting point, faster than writing from scratch.
A note on AI-generated "because" statements: The bootstrap prompt asks AI to hypothesize reasons for patterns it observes in your actual code files, not guess from generic training data. But these are still inferences, and some will be wrong. Treat the output as a first draft. When a "because" doesn't match reality, correct it. The exercise of reviewing and fixing these explanations often surfaces conventions you hadn't explicitly articulated.
Where this works best: Private codebases with conventions AI hasn't seen in training. For popular open-source projects, AI might already "know" the patterns from training data, making the bootstrap less revealing.
Source: Dev.to
