When a service returns 500, “AI, fix it” is tempting. Without a reproducible failure, any patch remains a guess. The first useful output from an AI coding assistant is often a testable explanation of when and where the flow breaks.
1. Capture the symptom
Write down expected and actual behavior, input, version, environment, time and the exact failing step. Remove secrets and personal data from logs before sharing context. For intermittent failures, record both the conditions that trigger and suppress them.
2. Limit the search area
Give yCode access only to permitted repository context, relevant files, a sanitized stack trace and a reproduction command. Ask for candidate functions and data transitions. Tell it what evidence is unavailable so it does not present a guess as a proven cause.
A sample prompt:
In staging, create-order returns 500 when an item lacks the optional
external_idfield. It should save successfully without that field. Here is a sanitized trace and the relevant handler. Give three possible causes. For each, name the function or line, a way to test it and an observation that would disprove it. Do not edit code yet.
3. Test the cheapest hypothesis first
Reproduce the failure with a minimal test or request. Distinguish “the symptom disappeared” from “the cause was removed.” A test that passes only with a new mock or a different environment is weak evidence. Inspect input contracts and adjacent execution paths when needed.
4. Request a minimal patch after diagnosis
Once the cause is supported, ask for a constrained change and a test that fails before the fix. Review the diff for unintended changes to authorization, error handling, logging and compatibility. Run the relevant tests and normal code review. Our earlier guide explains the merge check in more detail.
How Yasnora fits
yCode has beta capabilities for code understanding, repository search, run planning and diff review. Access and quality depend on plan, project context and configuration; an engineer approves any proposed change. It is available through Yasnora AI, part of the Wicsora ecosystem.
A useful completion criterion: the test that showed the bug fails before the change and passes after it, while the diff remains understandable to a human.
