I'm building a RAG app and need observability for retrieval quality and hallucinations

For a RAG app, you’ll usually want observability in two layers:

1. Retrieval quality — did the system fetch the right context?
2. Generation quality / hallucinations — did the model answer only from retrieved evidence?

What to track

Retrieval metrics

• Recall@k / Hit@k: whether the correct source appears in top-k retrieved chunks.
• MRR / nDCG: ranking quality of relevant chunks.
• Context precision: how much of retrieved context is actually relevant.
• Context coverage: whether retrieved chunks cover the answer sufficiently.
• Query-to-doc similarity stats: embedding similarity distributions, drift, low-confidence retrievals.
• Source diversity: whether all results come from one doc when multiple sources are expected.

Generation / hallucination metrics

• Faithfulness / groundedness: is each claim supported by retrieved context?
• Answer relevance: does the answer actually address the question?
• Citation accuracy: are citations pointing to the right chunk/source?
• Unsupported-claim rate: number or percentage of answer statements not backed by context.
• Refusal correctness: when context is insufficient, does the model say so?
• Hallucination severity: minor embellishment vs. fabricated facts.

How to instrument it

Log these artifacts for every request:

• user query
• retrieved chunks + scores + metadata
• reranker scores if used
• final prompt/context
• model output
• citations / chunk references
• latency and token usage
• user feedback, thumbs up/down, corrections

Useful evaluation setup

Offline

Build a test set with:

• queries
• expected answer
• gold supporting documents / chunks
• “unanswerable” queries

Run:

• retrieval evaluation on labeled data
• faithfulness checks on outputs
• regression tests per prompt/model/retriever version

Online

Monitor:

• low retrieval confidence
• answer length vs. evidence length
• high unsupported-claim rate
• user edits/corrections
• fallback/refusal frequency
• drift in embeddings or query types

Common implementation approach

You can score outputs with:

• LLM-as-judge for groundedness and relevance
• rule-based checks for citations, quotations, and unsupported claims
• embedding-based retrieval metrics
• human review for a sampled subset

Recommended architecture

Use an observability pipeline that stores:

• request traces
• retrieval traces
• generation traces
• evaluation scores
• feedback events

Then expose dashboards for:

• retriever performance by query type
• hallucination rate over time
• bad-answer examples
• top failing documents / sources
• prompt/model version comparisons

Tools people often use

Depending on your stack:

• LangSmith
• Arize Phoenix
• OpenTelemetry + your warehouse
• Weights & Biases
• Helicone
• Ragas for RAG-specific evaluation
• TruLens
• DeepEval

Practical starting point

If you want the fastest path:

1. Log every retrieval + answer trace.
2. Add a small labeled eval set.
3. Compute:
   • Recall@k
   • faithfulness
   • answer relevance
   • unsupported-claim rate
4. Build a dashboard by retriever/model version.
5. Add alerts for spikes in hallucination indicators.

If you want, I can also give you:

• a minimal observability schema for RAG traces,
• a metrics checklist,
• or a reference implementation using LangChain/LlamaIndex + Phoenix/RAGAS.