LLM Evaluation
1 min read
Evaluating Generated Output
Generation Metrics
| Metric | What It Measures |
|---|---|
| Faithfulness | Does the response accurately reflect retrieved content? |
| Relevance | Does the response answer the question? |
| Groundedness | Is every claim supported by retrieved documents? |
Evaluation Approaches
Automated: Use LLMs to judge response quality (faster, scalable) Human evaluation: Manual review (slower, more reliable) A/B testing: Compare RAG variants with real users
Common Evaluation Tools
- Ragas: Open-source RAG evaluation framework
- LangSmith: Tracing and evaluation from LangChain
- TruLens: Evaluation and observability
- Custom LLM judges: Prompt an LLM to score responses
Testing Agents
| Test Type | Purpose | Approach |
|---|---|---|
| Unit tests | Individual tools work | Mock agent, test tool outputs |
| Integration tests | Agent uses tools correctly | Controlled scenarios |
| Scenario tests | End-to-end task completion | Representative tasks |
| Adversarial tests | Handle edge cases | Unusual inputs, failures |
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