Evaluating LLM systems: metrics, benchmarks, and best practices
Why public benchmarks tell you almost nothing about your app, which metrics actually predict production quality, and the evaluation practices that separate teams who ship confidently from teams who guess.
TL;DR: Benchmarks measure models; evaluation measures your system. A model topping a public leaderboard says little about whether your RAG pipeline, prompt, and tools produce good outputs on your traffic. The metrics that matter are the ones defined for your task, scored on your data — offline to catch regressions, in production to catch reality.
There's a persistent confusion between benchmarking a model and evaluating a system. A model's MMLU score is a fact about the model. Whether your app gives users correct, grounded, useful answers is a different question, and no leaderboard answers it. This guide is about answering the second question.
Benchmarks vs. system evaluation
- Benchmarks (MMLU, GSM8K, HumanEval, arena rankings) compare models on standardized tasks. Useful for picking a base model to start with.
- System evaluation measures your application — model plus prompt plus retrieval plus tools — on your inputs and your definition of quality.
The trap: choosing a model by its benchmark rank and assuming the app inherits that quality. It doesn't. Retrieval, prompt design, and tool wiring dominate real-world quality, and none of them show up in a public benchmark. And a system that aces your offline suite can still fail users, because AI systems fail in ways evals miss when the test set drifts from production.
The metrics that predict production quality
Pick metrics that map to how your app can fail:
- Correctness / accuracy — is the answer right? Use exact checks where a golden answer exists, an LLM judge where it doesn't.
- Faithfulness / groundedness — is the answer supported by retrieved context? The core RAG metric. (See RAG evaluation metrics.)
- Relevance — did retrieval surface the right context in the first place?
- Task completion — did the system accomplish the user's goal end to end? Especially for agents. (See the AI agent evaluation guide.)
- Safety — refusal correctness and resistance to manipulation. (See the LLM safety guide.)
- Cost and latency — quality you can't afford or that arrives too late isn't quality. (See tracking token cost.)
Best practices that hold up
- Evaluate the system, not the model — always score the full pipeline on your data.
- Build the dataset from real traffic — synthetic-only test sets miss how users actually behave. Seed it, then grow it from production failures. (See synthetic data generation with LLMs.)
- Score per dimension — separate correctness, groundedness, and safety; a blended "quality" number hides what broke.
- Run offline and in production — offline catches regressions before ship, production catches what your test set never anticipated. (See run LLM evals on production traces.)
- Close the loop — every production failure becomes a new test case, so the suite gets smarter over time.
- Version everything — tie scores to the prompt and model version that produced them, so you can tell which change moved the number. (See why A/B test LLM prompts.)
Quick reference
| Question | Use |
|---|---|
| Which base model? | Public benchmarks, as a starting point only |
| Is my app good? | System evaluation on your data and metrics |
| Is the answer grounded? | Faithfulness score against retrieved context |
| Did it complete the task? | Task-completion metric, end to end |
| Did a change help? | Versioned scores across prompt/model variants |
How Currai fits
Currai evaluates your system — it traces the full pipeline and scores each trace against the metrics you define, offline and on production traffic, with the same rubrics across both so results are comparable. Scores are tied to the prompt and model version that produced them, so you can see which change actually moved quality, and failed production traces become test cases automatically. See traces and evals in one place and the LLM evaluation playbook, or start with Currai free.
Frequently asked questions
What's the difference between a benchmark and an evaluation?
A benchmark compares models on standardized tasks; an evaluation measures whether your application produces good outputs on your data. A high benchmark rank doesn't mean your app is good, because retrieval, prompt, and tools dominate real-world quality.
Which LLM evaluation metrics actually matter?
The ones mapped to how your app fails: correctness, faithfulness/groundedness, retrieval relevance, task completion, safety, and cost/latency. Score them separately rather than as one blended number.
Should I trust public leaderboards to pick a model?
Only as a starting point for choosing a base model. Once you've built the system around it, evaluate the system on your own data — that's the number that predicts what users experience.
How do I keep my evaluation set from going stale?
Grow it from production. Every real failure you catch becomes a new test case, so the suite tracks how users actually behave instead of what you imagined at launch.