LLM and RAG Evaluation Dataset Template: From Logs to Release Gates

What belongs in an LLM evaluation dataset?

An evaluation case should identify the input, the evidence available at the time, the acceptable outcome and the behavior that must never occur. For retrieval-augmented generation, score retrieval separately from the final answer: a correct-sounding response does not prove the right source was retrieved.

Use a versioned dataset for release comparisons and production monitoring to discover new cases. LangSmith's documentation describes this offline/online distinction. The template below is platform-independent, with an optional mapping for teams using LangSmith. It extends the sampling method in evaluation sets from production logs.

A template you can copy

The examples are fictional. Do not copy real customer identifiers or confidential source passages into a dataset without the appropriate access and retention controls. A reference answer can contain several acceptable formulations; it need not prescribe one exact sentence.

Three cases that test different things

Add a fourth case in which the relevant source is absent. The correct result is an explicit limitation or escalation rather than an invented rule. Add a fifth where the user is not allowed to view a matching document. Retrieval quality must not improve by leaking restricted evidence.

Score retrieval, answers and actions separately

For retrieval, inspect whether a required passage appears in the retrieved set and whether restricted or obsolete material was excluded. For answers, inspect whether the conclusion is supported, includes the relevant exception and makes uncertainty visible. For actions, inspect actual side effects rather than trusting the model's description of what it did.

A practical record might hold three independent results: evidence_found, answer_supported and action_authorized. Do not collapse them immediately into one score. If the evidence was absent but the answer happened to be correct, that is useful diagnostic information rather than a clean pass.

Start with deterministic checks for identifiers, permissions and required fields. Use human review for ambiguous business judgments. An LLM judge can help prioritize review, but calibrate it against human decisions and keep its version, rubric and disagreement cases. A judge's confidence is not ground truth.

Turn results into a release decision

Choose gates before looking at the new model's scores. An illustrative policy could require zero unauthorized writes, zero cross-tenant disclosures, no regression on high-severity cases and no increase in median review time. These are suggested gates to adapt, not established industry thresholds.

Report results by cohort. A system that succeeds on 95 routine questions and fails on all five exception questions has a 95% overall score and a serious operational gap. Repeating the same easy examples makes that percentage look better without improving the system.

Keep a held-out set apart from prompt tuning. Once a failure becomes part of the development loop, record that fact rather than continuing to describe it as unseen evaluation. Save model settings, prompt version, retrieval configuration and corpus version with each experiment so a comparison can be reproduced.

Mapping the template to LangSmith

For a team using LangSmith, put the request in the example's inputs, expected behavior in reference outputs and cohort/severity/source version in metadata. Use evaluators to return named scores for the dimensions above. Validate the exact field and SDK names against the version you install; the logical contract is more durable than a copied SDK snippet.

The platform records experiments; the team still owns what “acceptable” means. A beautiful experiment dashboard cannot decide whether a missing policy exception matters to your customers.

How to grow the set

Review rejected answers, human overrides, escalations and incident reports. Add a case when it represents a distinct failure or important boundary. Preserve routine traffic too, so the suite does not become an unweighted collection of rare disasters. Record both the source of a case and why it was selected.

A small, explicit set is useful early. It is not evidence of universal reliability. Expand it as the workflow gains languages, permissions, document types and actions, and track cost per completed task alongside quality.

Source and method

LangSmith evaluation concepts, checked October 24, 2026, supports the distinction between curated offline examples and online production monitoring. The table, sample cases, scoring dimensions and release policy here are the author's proposed method.