Question 1

Beyond Ragas: how do you eval LLMs with LLM-as-judge, DeepEval, and human eval?

Accepted Answer

**Target Audience:** Engineers who have one eval framework running (often Ragas on a RAG endpoint) and now need to evaluate the rest of an LLM product: summaries, support replies, agents, anything without a neat retrieval score.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the judge-calibration and review-routing example below). DeepEval and G-Eval details were checked against the DeepEval docs, PyPI and the G-Eval paper via web search in October 2026; where only sec

Question 2

Evaluating RAG with Ragas: faithfulness, answer relevancy, context precision?

Accepted Answer

**Target Audience:** Engineers who run or are about to run Ragas on a RAG system and want to know what each number means, how it is computed, and what to fix when it moves.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (a hand-computed version of the four formulas below). Metric definitions were checked against the Ragas documentation pages for v0.2 to v0.4 via web search in October 2026; API details that only appeared in third-party guides are marked as such.

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Medium

Beyond Ragas: how do you eval LLMs with LLM-as-judge, DeepEval, and human eval?

Evaluating RAG with Ragas: faithfulness, answer relevancy, context precision?