How to Use AI to Triage and Reproduce a Bug Report

A developer has detailed a four-step AI-assisted workflow for triaging and reproducing bug reports, using large language models to generate structured summaries, reproduction scenarios, failing tests, and root-cause hypotheses. The pipeline reportedly reduced a complex async race condition debugging task to under 20 minutes, compared to an hour or more traditionally.

Bug reports arrive incomplete, ambiguous, or buried in noise. Before you can fix anything, you need to triage — figure out severity and ownership — and then reproduce, which is often the hardest part. AI won't magically reproduce a bug for you, but it will dramatically compress the time between "a user filed a ticket" and "I have a failing test case in front of me." Here's the exact workflow I use. Paste the entire bug report — stack trace, user description, logs, whatever you have — and prompt: Here is a raw bug report. Summarize it with: 1. One-sentence description of what is failing 2. Likely severity P1/P2/P3 with your reasoning 3. The component or service most likely responsible 4. What information is missing that would help reproduce this --- PASTE RAW BUG REPORT HERE This gives you a structured starting point in seconds, and the "missing information" output is gold — it's your follow-up checklist for the reporter. Once you have the triage summary, ask the AI to sketch a reproduction path. Provide any relevant code, schema, or config snippets alongside the report: Given this bug report and the following code snippet, write a step-by-step reproduction scenario. Format it as numbered steps a developer can follow from a clean environment. Flag any assumptions you're making. Bug summary: paste Step 1 output Code snippet: paste relevant code The "flag your assumptions" instruction is critical — it surfaces gaps you'd otherwise only discover after wasting 30 minutes on the wrong path. This is where the real leverage is. Take the reproduction scenario and prompt: Convert these reproduction steps into a failing unit or integration test in language/framework . The test should: - Set up the exact preconditions described - Call the code path that triggers the bug - Assert the incorrect behavior so the test fails until the bug is fixed Reproduction steps: paste Step 2 output You now have a regression test before you've written a single fix. That's the correct order. With a reproducible case in hand, prompt for root cause candidates: Here is a bug description and a failing test. List the 3 most likely root causes, ranked by probability. For each, describe what code change would confirm or rule it out. Bug: summary Failing test: paste test This turns a blank-stare debugging session into a structured investigation with a clear order of operations. The four prompts form a repeatable pipeline: raw report → triage summary → reproduction steps → failing test → root cause hypotheses . Each step's output feeds the next, so the AI has the context it needs at every stage. On a recent project I ran a gnarly async race condition through this pipeline and had a failing test in under 20 minutes — a task that usually burns an hour or more. The key discipline: don't skip Step 1. A structured triage summary forces you to confirm you understand the bug before you start poking at code. I break down one workflow like this every week in The AI Leverage Weekly — practical, no fluff, free. Subscribe: https://theaileverageweekly.beehiiv.com/subscribe?utm source=devto&utm medium=article&utm campaign=medium w18 https://theaileverageweekly.beehiiv.com/subscribe?utm source=devto&utm medium=article&utm campaign=medium w18