RAG Evaluation Metrics: A Practical Guide for Measuring Retrieval-Augmented Generation with Maxim AI

#discuss #programming #ai

Why this matters if you own a RAG feature

I’ve watched clean lab demos fall apart in production: the retriever brings back the wrong paragraph when a user types shorthand, the model fills gaps with confident fiction, and p95 latency creeps past your SLA the second traffic spikes. This guide is the pragmatic way I measure and stabilize RAG—so we ship fast, and earn trust.

If you need rails for this, start here:

Experiment and compare retrievers, prompts, chunking: Experimentation
Simulate real users and evaluate agents at scale: Agent Simulation & Evaluation
Trace, monitor, and alert on live quality: Agent Observability
Docs and SDKs to wire it in: Docs, SDK Overview
If you want a walkthrough: Book a Demo or Get started free

The short list I actually track

Retrieval relevance: Precision@k, Recall@k, Hit rate@k, MRR, NDCG. Run A/Bs in Experimentation and keep the best stack as your baseline.
Context sufficiency: Do the top‑k passages contain the facts your answer needs? Validate in Agent Simulation & Evaluation.
Generation quality: Groundedness/faithfulness, unsupported claim ratio, answer relevance. Score offline in Simulation, then keep an eye on live sessions with online evaluators in Agent Observability.
Ops reality: p50/p95/p99 latency, throughput, cost per query. Dashboards + alerts in Agent Observability.
User signals: Citation clarity, task completion, thumbs up/down. Sample and route low‑score sessions to human review queues in Observability.

If you track just these, you catch the real failures early—before your users do.

How I separate retrieval vs. generation (and stop the blame loop)

First, fix retrieval. If Precision@5 or NDCG@10 is weak, the generator can’t save you. Once retrieval is solid, measure the generator’s faithfulness and usefulness.

Retrieval checks: Precision@k, Recall@k, Hit rate@k, MRR/NDCG@k, and diversity for multi‑hop and composite queries. Rapidly compare chunking, embedding, reranking in Experimentation.
Generation checks: Source attribution accuracy, unsupported claim ratio, entity/number consistency, contradiction detection versus retrieved context. Configure evaluators and rubrics in Agent Simulation & Evaluation.

Promote the combo that wins on both accuracy and latency. Keep it as your baseline in Experimentation so you can roll back if a new “optimization” isn’t actually better.

Targets that won’t waste your week

Precision@5 ≥ 0.70 for narrow enterprise KBs.
Recall@20 ≥ 0.80 for broader corpora.
NDCG@10 ≥ 0.80 when reranking is enabled.
Groundedness ≥ 0.90 in regulated domains.
Unsupported claim ratio ≤ 0.05 for high‑stakes flows.
p95 latency under your product budget (and visible in Agent Observability).

Tune based on your domain, cost ceiling, and SLA reality.

A pipeline you can implement this week

1) Build a golden set

Pull real queries from logs. Add typos, shorthand, and multi‑hop questions you see in support tickets.
Label a small, authoritative set of relevant passages per query, with provenance and doc versions.
Keep this versioned in your Maxim workspace; see “Run tests on datasets” in the Docs.

2) Run structured evals

Compare retrievers, chunking, rerankers, and prompts in Experimentation.
Simulate multi‑turn flows and tool calls in Agent Simulation & Evaluation.
Use prebuilt metrics (relevance, groundedness, answer quality) and add custom evaluators; reference the SDK Overview.

3) Gate deployments

Block deploy if Precision@5 or NDCG@10 drops vs. baseline, or groundedness dips/unsupported claims spike.
Canary and shadow traffic for risky changes. Trigger runs from CI with the SDK: Trigger test runs using SDK.

4) Observe live

Trace retrieval+generation spans with Agent Observability.
Sample sessions for online evaluators and route alerts to Slack/PagerDuty.
Export CSV/APIs for audits and BI; see Observability data export in the Docs.

When metrics fight each other (and what to do)

You will trade recall for latency and NDCG for tokens. My rule:

Plot latency percentiles next to NDCG/Precision in the same dashboard (Observability).
Maintain two baselines: functional (accuracy, groundedness) and operational (latency, cost) in Experimentation.
Promote only when both baselines stay inside target bands. If not, split traffic, measure cohorts in Agent Simulation & Evaluation, then decide.

FAQs I keep getting

Why is RAG evaluation tougher than plain LLM? You measure two systems plus their interaction. Retrieval decides evidence; generation decides trust; latency/cost decide feasibility. You need all three. Build the evaluations in Experimentation, then watch them live in Agent Observability.
What are must‑have retrieval metrics? Precision@k, Recall@k, Hit rate@k, MRR, NDCG@k, and context sufficiency. Run side‑by‑side stacks in Agent Simulation & Evaluation, share results via Analytics exports.
How do I measure faithfulness without overfitting to judges? Link claims to sources (source attribution accuracy), penalize unsupported claims, check entity/number consistency and contradictions, and use sentence embeddings for open‑ended semantic alignment. Mix LLM‑as‑judge with deterministic checks in Agent Simulation & Evaluation; keep online scores visible in Agent Observability.
How do I set baselines and avoid analysis paralysis? Freeze baseline v1 on a stable stack in Experimentation. Gate deploys on deviations. Rebaseline only when you change index schema or model families. Automate via SDK: Docs, SDK Overview.
How does Maxim help me keep this running? It unifies structured evals (Experimentation), trajectory‑level testing at scale (Agent Simulation & Evaluation), and real‑time tracing + alerts (Agent Observability). If you want help setting it up for your stack: Book a Demo or Get started free.