MongoDB’s AI Code Quality Fix The Chain of Repair
AI Summary
Summary of Video: “RysYyJwsigI”
- Topic: Code Quality and LLMs (Large Language Models) at MongoDB.
- Concern: Code quality generated by LLMs, especially in tests.
- Solution: Developed a chain of repair for code generated by LLMs:
- Custom evaluation metric established over time.
- Iterative process to improve generated code until it meets a pre-defined score.
- Automated repairs suggested by the LLM until quality criteria are met.
- Human intervention only occurs if the automated process fails to achieve the required quality score.
- Benefits: Ensures that code passes quality checks before testing, reducing chances of failure in tests due to poor quality.
This process enhances the reliability of automatically generated code.