Workflow
How to read a build error yourself before asking AI to suggest the fix
One of the easiest ways to waste time with AI debugging is to forward a raw build log before you have personally identified the first meaningful error. Build systems are noisy by design. They emit context for machines, framework internals, caches, dependency resolution, loader chains, and runtime wrappers. If you let the model consume all of that without a first pass, you are outsourcing the part of the task that is easiest for a human to do quickly.
The first pass is simple. Find the first error message that directly names a missing module, failing import, invalid type, syntax problem, unresolved file, or broken command. Ignore repeated downstream failures until you have found that first cause candidate.
1. Find the trigger
Look for the first explicit failure, not the most dramatic summary at the bottom of the output.
2. Keep the location
Preserve the file path, line number, route, command, or Docker step that locates the failure in your project.
3. Add one sentence
Tell the model what you expected to happen and what changed recently.
Where a cleaner fits into this workflow
A cleaner is most valuable after the first read, not before it. Once you have found the likely trigger, removing the rest of the noise helps you package that signal into a prompt that is easier to reason about. That is where ContextClean is intentionally useful: it compresses noisy output so the real issue is easier to review and share.