Evaluating LLM Outputs: RAGAS, DeepEval, and Custom Metrics

1-minute read
llm • evaluation • ragas • deepeval • ai

Frameworks like RAGAS and DeepEval codify LLM evaluation metrics so you can regression-test prompts and pipelines in CI.

RAGAS (RAG Assessment)

Measures context precision/recall, faithfulness, and answer relevance-ideal for retrieval pipelines.

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy

result = evaluate(dataset=eval_dataset, metrics=[faithfulness, answer_relevancy])

DeepEval

Offers pytest-style LLM tests, G-Eval, and hallucination metrics with CI integration.

Custom Metrics

Domain-specific checks often outperform generic scores-JSON schema match, SQL execution success, unit test pass rate for codegen.

CI Integration

Run evals on PRs when prompts or retrieval change. Block merge on regression beyond a threshold.

Conclusion

Pick 3–5 metrics aligned with your product. Automate them early-manual spot-checking does not scale past prototype.