AI-Assisted Debugging Workflow

Production debugging often starts with scattered context: an issue description, logs, recent changes, database signals, and tribal knowledge from previous incidents. AI can help organize that context, but only when the workflow is structured and reviewed by engineers.

This project focused on using AI as an investigation assistant, not as an unchecked decision maker.

Problem

Debugging production issues can be slow when engineers have to manually collect issue details, inspect logs, check related data, and convert findings into a root-cause-analysis flow.

The important engineering concerns were:

• Gather useful context consistently.
• Summarize signals without losing important details.
• Keep the engineer in control of decisions.
• Produce fix-plan suggestions that can be reviewed.
• Help other engineers adopt the workflow.

Approach

I built an AI-assisted debugging workflow that combined issue context, operational signals, database checks, and structured RCA prompts. The workflow helped produce investigation summaries and possible fix plans for engineer review.

The design focused on:

• Context collection from engineering tools.
• Log and issue summarization.
• Structured RCA prompts.
• Database checks where appropriate.
• Setup guides and team adoption support.

Engineering Value

This work reduced manual investigation effort by making debugging more repeatable. It also helped engineers move from scattered signals to a clearer RCA path while keeping human review at the center of the workflow.

Skills Demonstrated

• AI-assisted engineering workflows.
• Production debugging.
• Prompt design.
• RCA preparation.
• Engineering enablement.