Eval Framework
Systematic framework for evaluating LLM outputs against defined criteria like correctness, relevance, safety, and style.
Eval frameworks automate LLM quality assurance – for consistent outputs, regression tests, and model comparisons.
Explanation
Eval frameworks automate quality assurance for AI applications. Methods: golden dataset comparison, LLM-as-judge (AI evaluates AI), semantic similarity. Tools: Promptfoo, Braintrust, RAGAS for RAG systems. Enable CI/CD for prompts and models.
Marketing Relevance
Indispensable for iterative prompt development. Prevents regressions. Objective basis for model comparisons.
Example
Content team defines eval suite: checks if generated texts match brand voice, contain no hallucinations, include CTAs.
Common Pitfalls
LLM-as-judge can have own biases. Test sets become outdated. Metrics don't always correlate with user satisfaction.
Origin & History
Emerged 2023 as response to non-deterministic LLM outputs. Promptfoo, Braintrust, and RAGAS became leading open-source tools.
Comparisons & Differences
Eval Framework vs. Unit Tests
Eval frameworks assess semantic similarity and quality; unit tests check exact, deterministic outputs.
Eval Framework vs. A/B Testing
Eval frameworks test quality before deployment; A/B tests measure user reactions in production.