LLM Evaluation Framework
A systematic framework for evaluating language model outputs across correctness, safety, and cost dimensions.
AI
Problem
"Does this LLM output look right?" is not an engineering answer. Teams need systematic evaluation.
Approach
Built a framework that treats LLM evaluation like software testing:
- Test suites: Input-expected output pairs, versioned alongside prompts
- Evaluators: Pluggable scoring — exact match, semantic similarity, LLM-as-judge
- Runners: Parallel execution across multiple model providers with cost tracking
Results
- Identified a prompt variant that improved correctness by 15% at 40% lower cost
- Caught 3 regressions that would have shipped to production