I need to monitor RAG retrieval quality, chunk relevance, and citations

To monitor RAG retrieval quality, chunk relevance, and citations, set up evaluation at three layers:

1) Retrieval quality

Measure whether the retriever is finding the right source material.

Core metrics

• Recall@k: Did the correct source appear in the top-k results?
• MRR (Mean Reciprocal Rank): How high is the first relevant chunk ranked?
• Precision@k: How many of the retrieved chunks are actually relevant?
• Hit rate@k: Did any relevant chunk appear in top-k?

What to log

• User query
• Retrieved chunk IDs / document IDs
• Retriever scores
• Top-k ranking order
• Ground-truth relevant docs/chunks, if available

Best practice

• Build a labeled eval set of queries with known relevant sources.
• Slice results by query type, document type, and topic.

---

2) Chunk relevance

Measure whether each retrieved chunk is actually useful for answering the query.

Ways to assess

• Human labels: relevant / partially relevant / irrelevant
• LLM-as-judge: ask a model to score each chunk against the query
• Embedding similarity: useful for debugging, but not enough alone

Useful metrics

• Relevance rate of top-k chunks
• Average relevance score per query
• % of queries where at least one highly relevant chunk is retrieved
• Redundancy rate: how many chunks repeat the same information

Chunk-level rubric

• 2 = directly answers or contains key evidence
• 1 = somewhat related / supporting context
• 0 = irrelevant

---

3) Citation quality

Measure whether the final answer is properly grounded in retrieved sources.

Metrics

• Citation coverage: % of answer claims backed by citations
• Citation correctness: cited source actually supports the claim
• Citation precision: how often citations are needed vs over-cited
• Unsupported claim rate: statements with no supporting citation
• Citation diversity: are citations spread across sources appropriately?

What to check

• Each factual claim maps to one or more retrieved chunks
• Citations point to the exact chunk or source used
• No hallucinated citations
• Citations are not just present, but relevant

---

4) Recommended monitoring pipeline

1. Log retrieval output for every query
2. Run offline evaluation on a labeled benchmark set
3. Use LLM-based judging for chunk relevance and answer grounding
4. Track trends over time in a dashboard
5. Alert on regressions after prompt, retriever, embedding, or index changes

---

5) Example dashboard columns

For each query:

• Query text
• Top-k retrieved chunks
• Retriever scores
• Relevant chunk found? Y/N
• Best rank of relevant chunk
• Answer citations
• Unsupported claims count
• Overall answer faithfulness score

---

6) Practical tooling

You can implement this with:

• RAGAS: retrieval + faithfulness-style metrics
• TruLens: groundedness, relevance, feedback functions
• LangSmith: tracing + evals
• Arize Phoenix: retrieval and embedding diagnostics
• Custom evals with an LLM judge

---

7) Minimal metric set to start

If you want a simple setup, start with:

• Recall@5
• MRR
• Top-5 chunk relevance rate
• Citation coverage
• Unsupported claim rate

If you want, I can also provide:

1. a JSON logging schema for RAG traces,
2. a Python eval script, or
3. a dashboard design for these metrics.