Evaluate LLM Code Generation with LLM-as-Judge Evaluators
This tutorial shows how to score AI-generated code against custom criteria to determine the best model for your specific needs, such as security, API adherence, and avoiding scope creep.
Why it matters
This approach enables companies to move beyond generic LLM benchmarks and find the most suitable model for their unique codebase and requirements.
Key Points
- 1Set up a proxy server to route code generation requests through LaunchDarkly and score responses with custom judges
- 2Build judges to check for security vulnerabilities, API contract adherence, and unnecessary changes
- 3Use the scoring data to choose the best AI model for your codebase and use cases
Details
The article describes a system that allows developers to evaluate different large language models (LLMs) for code generation tasks specific to their needs. It involves setting up a proxy server that routes code generation requests through LaunchDarkly, an AI configuration platform. The proxy extracts text-only prompts, routes them through LaunchDarkly's model selection, invokes the chosen model, and triggers custom judges to evaluate the generated code. The judges check for security vulnerabilities, API contract adherence, and unnecessary changes or scope creep. Over time, this allows developers to build a dataset on how different models perform for their specific use cases and choose the best one accordingly.
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