The useful output of an evaluation is a defensible decision: ship, revise, or investigate. Build your shortlist around the evidence your team needs to make that decision.
AI teams building repeatable quality checks
Start with representative cases
Use real failure patterns and common successful tasks, with clear expected behavior.
Keep a baseline
A candidate score means little without a comparable baseline, fixed inputs, and known model settings.
Inspect disagreements
Review cases where an automated score conflicts with human judgment or customer feedback.
At a glance
| Need | Shortlist focus | Evidence to request |
|---|---|---|
| Connected tracing and experiments | LangSmith or Langfuse | A failure converted into a compared test case |
| Prompt and conversation investigation | Lunary | A customer issue turned into a reviewed prompt test |
| Gateway-linked datasets | Helicone | Representative requests selected for an experiment |
| Business-critical release gates | Every candidate | A reproducible decision on known regressions |
Decide what quality means for your product
A support assistant, a structured extraction task, and an action-taking agent need different evaluation criteria. Define correctness in terms of the task: an answer supported by the supplied material, a field extracted exactly, or an action performed with the right arguments. Split deterministic requirements from judgments that need a person or model. Avoid collapsing every failure into a single average score.
Build the test set before choosing the dashboard
Collect common tasks, costly failures, and important edge cases. Keep the observed model output separate from the expected correct behavior. Document who approved reference answers and which examples should remain held out from prompt development. Include negative cases where the right behavior is to ask for more context or decline an unsupported action. A polished evaluation interface cannot repair a misleading dataset.
Compare tools with the same release exercise
LangSmith documents dataset-based evaluations and experiments. Langfuse documents scores and evaluation workflows. Lunary documents prompt testing and dataset import capabilities that can support a trace-to-test workflow. Helicone documents datasets and prompt management. For each candidate, run the same baseline and prompt change, inspect regressions, and identify the steps your team would still need to implement outside the product.
Check your evaluators as carefully as your prompts
A model judge can be useful, but its score is another model output. Validate a sample against human decisions, record the judge prompt and model, and inspect disagreement by failure type. Use deterministic checks for properties such as JSON validity or required fields when possible. Repeat stochastic tests when the expected variation could change your release decision, and include evaluator model usage in the cost estimate.
Make the result actionable
Before running the candidate, define what would block the release. A small average improvement should not excuse a new failure in a critical task. Record the baseline, candidate configuration, dataset version, evaluator settings, and the reviewed exceptions. After release, look for failures that the dataset missed and add them through the same review process. The goal is a better decision process that learns from production.
Common questions
Should I use an LLM judge for every check?
No. Deterministic checks are often more appropriate for exact structure and required values. Use model or human judgment for criteria that cannot be fully expressed that way.
How many evaluation examples do I need?
There is no universal number. Cover the tasks and failure modes that can change the decision, then increase coverage where uncertainty remains material.
Can a higher evaluation score prove business impact?
No. It is evidence about the selected test criteria. Validate adoption, task success, and customer outcomes after release using product instrumentation.
Sources & methodology
Lunary publishes this guide. We compare documented workflows and explain where each approach fits; this is not an independent benchmark or a hands-on product rating. Features, limits, and commercial terms can change. Check the linked vendor documentation before deciding.