Editing Rag (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Expert RAG evaluation requires separate assessment of the retrieval and generation components:

'''Retrieval metrics'''
* '''Recall@k''': Of the relevant chunks that exist, what fraction was retrieved in the top-k?
* '''MRR (Mean Reciprocal Rank)''': How highly is the first relevant chunk ranked?
* '''Precision@k''': Of the k retrieved chunks, what fraction was actually relevant?

'''Generation metrics'''
* '''Faithfulness''': Does the generated answer only assert things supported by the retrieved context? (Use an LLM as judge)
* '''Answer relevance''': Does the response actually address the user's question?
* '''Context relevance''': Are the retrieved chunks relevant to the query?

'''RAGAs framework''': The open-source RAGAs library automates evaluation of faithfulness, answer relevance, and context precision/recall using LLM-as-judge.

Expert practitioners run '''end-to-end evals''' on a curated test set of query-answer pairs, tracking all metrics over time and alerting on regressions as the knowledge base evolves.
</div>

<div style="background-color: #2F4F4F; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">