Checklist
An AI debugging checklist for sending better logs and getting more useful answers
A model cannot fix what your prompt does not explain. At the same time, a model does worse when the prompt includes too much irrelevant context. The useful middle ground is not “paste less” in the abstract. It is a repeatable checklist for keeping the lines that matter and removing the ones that only increase noise.
This page is written for developers who already use tools such as ChatGPT, Claude, Cursor, and Codex and want a more reliable workflow for debugging code with AI support.
Step 1
Identify the first clear error, not the last repeated frame.
Step 2
Keep the failing command, file path, and line number when available.
Step 3
Remove dependency internals, install progress, and irrelevant warnings.
Step 4
State what behavior you expected and what changed recently.
Step 5
Review the text for secrets, tokens, customer data, or internal URLs.
Step 6
Ask the AI for one specific job: root cause, minimal fix, or verification steps.
Why this improves results
Models respond to the shape of the context they receive. A prompt that begins with the real error, identifies the affected file, and states the expected behavior is easier to reason about than one that starts with 200 lines of terminal chatter. The point is not to hide complexity. The point is to make the important complexity visible first.
ContextClean helps with the reduction step, but the final prompt still benefits from explicit human framing: what broke, when it started, and what kind of answer you want next.