How I use LLMs for structured classification without getting garbage output

1. 1Read the PR title, labels, and body carefully using a natural language processing tool
2. 2Explain what the change does for the end user in one sentence using an LLM
3. 3Classify the PR as one of feature, improvement, bugfix, breaking, or internal using the LLM's output
4. 4Return the classification result as JSON
5. 5Batch multiple PRs per request to improve cost and speed

Data scientists and machine learning engineers on a team can benefit from this approach to improve the accuracy of their LLM-based classification models, while product managers can use this technique to automate tasks such as categorizing GitHub pull requests

💡 Forcing the model to articulate user impact before labeling makes it actually read the PR body, resulting in more accurate classifications

💡 Improve LLM-based classification accuracy by forcing the model to articulate user impact before labeling #LLMs #MachineLearning