The ultimate LLM evaluation playbook

TL;DR: A complete LLM evaluation program has six parts: define what "good" means, build a dataset from real usage, choose the right metrics and scoring methods, run evals both offline and on production traffic, close the loop by turning failures into test cases, and assign clear ownership. This playbook walks through each so you can assemble an evaluation practice that actually improves your AI over time.

Most teams "do evaluation" as a scattered set of habits — a few test cases here, an occasional manual review there — and wonder why quality doesn't improve. Evaluation that compounds is a system: each part feeds the next, and failures make it better. This playbook lays out that system end to end, drawing together the pieces covered across our other evaluation guides.

The six parts of an evaluation program

Part 1: Define quality

Everything starts here. Write down the dimensions that matter for your use case (accuracy, grounding, safety, tone, task success), how they're weighted, and the threshold that ships. This is a product judgment, not a technical one, and it's the foundation the rest rests on. (See LLM product manager workflows and evals for PMs and AI product quality.)

Part 2: Build datasets

You need two kinds of test data:

• A golden dataset — curated inputs with human-reviewed reference outputs, your stable benchmark. Start small (20–30 real cases) and grow it. (See improve golden datasets with human review.)
• Production traffic — real requests, evaluated continuously, so you test the living distribution, not a frozen snapshot.

Build both from real usage wherever you can; synthetic-only datasets miss reality.

Part 3: Choose metrics and scoring methods

Match the metric to what matters, and the method to the metric.

Metrics by system type:

• General generation: accuracy, relevancy, safety, tone.
• RAG: answer relevancy, faithfulness, contextual precision/recall/relevancy. (See RAG evaluation metrics.)
• Agents: outcome, trajectory, tool use, safety. (See the definitive AI agent evaluation guide.)
• Conversations: context retention, consistency, goal completion. (See multi-turn LLM evaluation.)

Scoring methods:

• Reference-based when you have ground truth.
• LLM-as-judge for open-ended quality (audit the judge — see human-in-the-loop AI agent evaluation).
• Comparison-based for choosing between versions. (See LLM arena-as-a-judge.)

Part 4: Run evals — offline and in production

• Offline — run against your golden set to catch regressions before shipping, ideally in CI. (See best AI eval tools for CI/CD.)
• Production — evaluate real traffic continuously, because offline sets miss distribution shift, integration failures, and unmeasured dimensions. (See three ways AI systems fail even when evals pass and run LLM evals on production traces.)

You need both: offline to gate changes, production to catch reality.

Part 5: Close the loop

This is what makes evaluation compound. Every failure you find — offline or in production — becomes a new test case, so the same bug can't ship twice. Your dataset grows from real failures, your rubric gains dimensions from unmeasured ones, and your judge stays calibrated against human review. Evaluation that doesn't feed back is just measurement; the loop is what improves the product. (See turn production traces into better AI.)

Part 6: Own it

Assign clear ownership. Quality is a product responsibility, usually a PM's, not a task that belongs to everyone and therefore no one. As you scale, evaluation becomes a shared team practice. (See PMs should own AI eval loops and evals are a team sport.)

The playbook as a weekly rhythm

Assembled, the program runs as a cadence:

1. Score production traffic and the golden set.
2. Review flagged and low-scoring traces.
3. Add new failures as test cases; extend the rubric where needed.
4. Experiment — A/B test prompt or model changes against the evals.
5. Ship verified improvements; report the quality trend to stakeholders.

Each cycle makes the eval set sharper and the product better.

How Currai fits

Currai is the platform this playbook runs on: define rubrics, build datasets from real traffic, score with reference, LLM-as-judge, and comparison methods, run evals offline and on production traces, turn failures into test cases, and give PMs and engineers a shared place to own quality. The whole loop — tracing, evaluation, experimentation — in one system, so evaluation compounds instead of scattering. See traces and evals in one place and the AI agent observability guide, or start with Currai free.

Frequently asked questions

What are the parts of a complete LLM evaluation program?

Six: define quality (rubric and thresholds), build datasets (golden plus production), choose metrics and scoring methods, run evals offline and on production, close the loop by turning failures into test cases, and assign clear ownership.

Do I need both offline and production evaluation?

Yes. Offline evals against a golden set catch regressions before shipping; production evaluation catches distribution shift, integration failures, and unmeasured dimensions that offline sets structurally miss. They serve different purposes.

What makes evaluation actually improve a product?

The feedback loop. Turning every failure — offline or in production — into a new test case means the same bug can't ship twice, your dataset grows from reality, and your rubric gains the dimensions it was missing. Measurement without feedback doesn't compound.

Who should own LLM evaluation?

Quality is a product responsibility, usually a PM's, with clear single ownership so it doesn't fall between teams. As you scale, evaluation becomes a shared practice across product, engineering, and QA.