Debugging Is the Killer App for Free Model Tokens — Here's the Workflow

Description and use

The article suggests using free model tokens for debugging rather than code generation. It proposes a workflow: collecting logs, extracting errors, creating a prompt with context, and querying an OpenAI-compatible API for ranked hypotheses on the root cause.

Problem

Developers often waste free model tokens on code generation, which requires significant review and testing, while debugging is a high-leverage, pattern-matching task where models can provide immediate, actionable diagnoses to unblock development.

Outcome

A reproducible Python-based workflow that converts error logs into ranked debugging hypotheses using free model tokens, aiming to reduce debugging time by leveraging the model's pattern-matching capabilities.

Workflow

• Collect the logs: Pull the last hour of logs or the log file from the failed run.
• Extract the error block: Find the most recent exception or error, including context lines before the stack trace and the full trace itself.
• Build a prompt: Include the error block, project structure hints, and a request for ranked hypotheses.
• Call the model: Use an OpenAI-compatible endpoint with a Python script to send the prompt.
• Verify the top hypothesis: Try the suggested fix; if it fails, ask a follow-up question with the new error message.

Evidence

A model that reads your error logs and produces a ranked list of hypotheses can save you more time than any code snippet it generates

Source

Debugging is fundamentally a pattern-matching exercise.

Source