RAG Evaluation UX

Introduction

Designing Retrieval‑Augmented Generation (RAG) evaluation for product teams is a UX problem as much as it is an MLOps problem. This page defines the core metrics (faithfulness/groundedness, answer relevance, precision/recall), a standard "run‑card" UI spec, and implementation links for LangSmith and TruLens so teams can ship trustworthy, comprehensible evaluation surfaces.

What to measure in a RAG system

Faithfulness / Groundedness: Does each claim in the model’s answer trace back to retrieved context? Reference‑free LLM‑as‑judge prompts are common for this check. See LangSmith’s evaluator concepts and reference‑free guidance and TruLens’s RAG Triad definition of groundedness.
Answer Relevance: Does the answer address the user’s query? See LangSmith evaluator options and TruLens Quickstart.
Context Relevance: Are retrieved chunks relevant to the query? TruLens evaluates this directly; LangSmith supports custom evaluators and prebuilt LLM‑as‑judge prompts.
Retrieval Quality (IR metrics): Precision@k, Recall@k, MRR/nDCG using ground‑truth query→doc pairs. TruLens provides ground‑truth evaluation quickstarts for retrieval systems.
Operational signals: Latency (P50/P95), cost per query, cache hit rates, embedding/index staleness, reranker contribution. Track alongside quality metrics to avoid regressions that “look better” but slow or over‑spend in prod.

Metric-to-UX mapping

Metric	What it validates	Inputs	Typical method	Useful UI affordances
Faithfulness / Groundedness	Answer is supported by retrieved context	question, context, answer	LLM‑as‑judge with claims + evidence or NLI	Line‑level claim highlights, evidence hover, unsupported‑claim badges
Answer Relevance	Answer addresses the question	question, answer	LLM‑as‑judge scoring	Relevance score pill, query restatement diff
Context Relevance	Retrieved chunks fit the query	question, retrieved chunks	LLM‑as‑judge or embedding similarity	Per‑chunk score chips, reorder visualization
Precision@k	% retrieved that are truly relevant	query, retrieved set, gold set	IR metric	Threshold gates in run summary
Recall@k	% of all relevant retrieved	query, retrieved set, gold set	IR metric	Heatmap over k; alert if below gate
Cost/Latency	Operability in prod	traces, spend, timings	instrumentation	P50/P95 bars; per‑stage breakdown

References: LangSmith evaluation concepts; prebuilt evaluators; custom evaluators; online evaluations. TruLens RAG Triad; Quickstart; Ground Truth (retrieval) quickstarts; summarization eval patterns.

The run‑card UI (standard spec)

A run‑card is a compact, shareable summary of one evaluation run. It should be readable in 10 seconds, explorable in 2 minutes.

Include:

Identity: Run name, permalink, author, time range, dataset snapshot ID (immutable), environment (dev/stage/prod), branch/commit, model + retriever versions (embedding model, top‑k, chunking, reranker), index build timestamp.
Gates: Clear pass/fail against target thresholds (e.g., Faithfulness ≥ 0.85, Recall@10 ≥ 0.60, P95 latency ≤ 5s). Show deltas vs. baseline.
Summary: Macro aggregates with confidence bands: Faithfulness, Answer Relevance, Context Relevance, Precision@k/Recall@k, cost/query, P50/P95 latency.
Top regressions/improvements: Ranked by metric impact with links to examples.
Data coverage: Query domains, language mix, length distribution; drift vs. previous run.
Provenance: Prompt/version hashes, guardrails/filters, temperature and decoding params.
Compliance: Evaluation provider (model name), prompting template, and disclaimers for LLM‑as‑judge variance.
Export & share: CSV/JSON export; copyable share link; run notes.

Drill‑down anatomy (sample view)

Query panel: Original question, expected answer (if any), constraints.
Retrieval panel: Ordered context chunks with source URIs and per‑chunk relevance scores; filters to view top‑k variants and reranker effects.
Answer panel: Model response with claim segmentation; each claim linked to supporting spans; unsupported claims highlighted.
Scoring panel: Faithfulness, Answer Relevance, Context Relevance with rationales; IR metrics if ground truth exists; raw judge outputs available.
Timing & cost: Stage‑level breakdown (embed → retrieve → rerank → generate).
Actions: Create issue, add to golden set, re‑run with alt prompt/model, compare vs. another run.

Design patterns that improve evaluator UX

Evidence‑first presentation: Default to showing answer text with inline citations and a compact context viewer so reviewers can verify support quickly.
Gate badges: Prominent, color‑coded pass/fail with tooltips showing thresholds and links to the policy.
Pairwise diff: Side‑by‑side answers with per‑claim verdicts and net score delta; hide‑unchanged toggle.
Cohort slices: Segment by query type, language, length, or customer to reveal pockets of failure.
Determinism guardrails: Surface judge model, prompt, temperature, and seed; warn when configs drift between comparable runs.
Human‑in‑the‑loop: One‑click corrections to golden sets and judgement overrides with audit trail.

Implementing evaluators in Lang

Smith

Concepts and taxonomy: See Evaluation concepts (reference‑free vs. reference‑based; online vs. offline; pairwise) and the RAG‑specific evaluator summary.
Prebuilt evaluators: LangSmith integrates openevals and supports off‑the‑shelf LangChain evaluators (e.g., correctness, relevance, exact match, embedding distance). See Off‑the‑shelf evaluators and How to use prebuilt evaluators.
Custom metrics: Return numerical or categorical metrics; emit multiple metrics from one function. See How to define a custom evaluator and Metric types.
Running at scale: Bind evaluators to datasets in the UI to auto‑grade experiments; set up online evaluations for real‑time feedback in production.

Helpful links:

LangSmith: Evaluation concepts; Off‑the‑shelf evaluators; Prebuilt evaluators; Custom evaluator; Metric types; Online evaluations; Bind evaluators to datasets.

Implementing evaluators in Tru

Lens

RAG Triad: Context Relevance, Groundedness, Answer Relevance—reference‑free checks central to hallucination control.
Quickstarts: End‑to‑end RAG with feedback; LangChain and LlamaIndex integrations; Ground‑truth retrieval evaluation (IR metrics) and Ground Truth evaluations for early experiments.
Additional patterns: Summarization evaluation (BLEU/ROUGE/BERTScore + groundedness), helpfulness/coherence/sentiment checks, and OpenTelemetry instrumentation.

Helpful links:

TruLens: RAG Triad; Quickstart; Ground Truth for retrieval; Ground Truth evaluations; Summarization evaluation; LangChain quickstart; LlamaIndex quickstart; NeMo guardrails instrumentation.

Why a “run‑card,” not just a model card?

Model cards document capabilities and limitations across a model’s lifecycle. A run‑card documents one evaluation execution—data snapshot, configuration, and results—so teams can compare runs and make ship/no‑ship decisions. See an example of model‑card workflows in Robust Intelligence’s documentation; run‑cards apply the same transparency at the experiment/run level.

From offline to online: a pragmatic path

1) Prove retrieval first (IR metrics). Establish Recall@k and Precision@k on a golden set before tuning prompts or models. 2) Add reference‑free judges (faithfulness, answer relevance, context relevance) for breadth. Track judge stability by fixing judge model and prompt. 3) Blend real traffic. Capture production queries, attach actual helpful docs clicked/read, and continuously expand goldens. 4) Gate releases. Enforce run‑card gates in CI/CD; require signed run‑cards for model/prompt/index changes. 5) Monitor in prod. Online evaluations plus P50/P95 latency and cost budgets; alert on drift or gate breaches.

How Zypsy helps founders ship evaluation UX

Zypsy is a design and engineering team for founders, with integrated brand→product→web→code delivery. We build evaluation surfaces that are transparent, explorable, and shippable to stakeholders, pairing design systems with practical MLOps constraints.

Startup‑native execution: Sprint delivery and end‑to‑end product craft across web and product UX, plus engineering integration (instrumentation, dashboards). See our capabilities.
AI & security experience: Multi‑year partnership with Robust Intelligence on brand, product, and embedded engineering across their AI risk and governance platform—work that demanded clear model/test reporting for enterprise stakeholders. See Robust Intelligence case.

Links: Zypsy capabilities, Robust Intelligence case, Work.

FAQ

What thresholds should we use for gates? Start from business risk. A common first gate is Faithfulness ≥ 0.8–0.9 and Recall@k ≥ 0.6–0.7 on a representative golden set, with P95 latency under your UX budget. Calibrate on your data; don’t cargo‑cult others’ numbers.
Do we need ground truth to start? No. Use reference‑free judges (faithfulness, answer relevance, context relevance) to bootstrap; add golden sets over time for retrieval IR metrics and answer correctness checks.
Aren’t LLM‑as‑judge scores unstable? They can be. Fix judge model+prompt+temperature, log rationales, and average over sufficiently sized datasets. Use pairwise comparisons to reduce variance and keep a small human spot‑check set for calibration.
How do we show evidence to non‑ML stakeholders? Prefer claim‑level highlights and per‑chunk relevance chips; keep the run‑card top section simple with pass/fail gates and deltas vs. baseline.
Can we compare two runs easily? Yes. Implement a pairwise diff view: align queries; show metric deltas and claim‑level changes; flag config drift (model, prompt, retriever, index timestamp).
How do we integrate this into releases? Treat the run‑card as an artifact in CI/CD. Require a passing run‑card for merges that change prompts, models, retrievers, or indices.

RAG Evaluation UX

Introduction

What to measure in a RAG system

Metric-to-UX mapping

The run‑card UI (standard spec)

Drill‑down anatomy (sample view)

Design patterns that improve evaluator UX

Implementing evaluators in Lang

Implementing evaluators in Tru

Why a “run‑card,” not just a model card?

From offline to online: a pragmatic path

How Zypsy helps founders ship evaluation UX

FAQ

Further reading