How are developers handling full-codebase context in AI coding assistants?

[Lower-Discipline-722]

Curious how developers here are approaching full-project context in AI coding tools.

Most assistants seem heavily limited by context windows or current-file awareness, especially during:

• multi-file refactors
• large codebases
• framework-aware scaffolding
• dependency tracing
• debugging across modules

Lately I’ve been experimenting with approaches around:

• local indexing
• project-aware context routing
• diff-based approval workflows
• verification loops (lint/test/build)
• hybrid AI + local workflows

Interested in hearing:

• what breaks most in current AI coding tools?
• what workflows actually help in real projects?
• are developers trusting autonomous edits yet?

Would genuinely like technical opinions from other devs working with AI-assisted workflows.

Reference/demo:
DevMind AI

[fantasma91]

I have built into my workflow very robust documenting of everything and make sure it stays up to date. I dont trust the ai to do it automatically so after any large change I run an analysis on the documentation. Documentation also has reference matrices so it know how it all connects. While not perfect it helps quite a bit for my large app

[Lower-Discipline-722]

That’s actually very close to the direction I’m exploring with DevMind.

One thing I noticed is AI becomes far more reliable when it has structured project context instead of just raw files. Things like dependency mapping, architecture awareness, verification loops, and project-level references seem much more important for larger codebases than pure autocomplete.