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I wrote my own faithfulness check by hand, and now every new metric I want means another script to write and debug.
What you'll have at the end
A test run scoring faithfulness and context precision from a maintained library instead of your own script.
You need
A batch of saved RAG rows, each holding the question, the chunks your retriever returned, and the answer your system generated, plus a hand-written script that checks each answer's claims against its context and prints a pass or fail line.
Not covered
Comparing this pair against a broader eval platform like LangSmith, or a dedicated red-teaming tool like Promptfoo, both of which solve a different problem than scoring one answer's claims and one retrieval's ranking.
Write your first LLM-as-a-judge grading script
start there first if you don't have a hand-rolled judge script yet to swap out
Does your fully automated LLM judge agree with a human reviewer?
go there once you want to check whether the library's own judge model agrees with a person before you trust its verdict
Wire a regression check that runs before every prompt edit ships
go there to build the CI gate itself, if you don't already have one for this verdict to feed
Checked 18 Aug 2026
Part of the AI-LLM App Evaluation cookbook