How to build an LLM evaluation framework from scratch

TL;DR: An LLM evaluation framework has four parts: a dataset of test cases, a set of metrics (deterministic and LLM-judge), a runner that scores outputs against them, and a store for the results so you can compare over time. Building the core is a weekend; building the parts that make it useful in production — trace integration, versioned comparisons, production scoring — is where homegrown harnesses stall. This is what to build, in order, and where to stop.

Rolling your own eval framework is a good way to understand evaluation deeply. It can also become a second product you maintain forever. This tutorial builds the framework in layers so you can see exactly where the value is and where the maintenance burden begins.

Part 1: The dataset

Everything starts with test cases. Each case is an input, and — where one exists — an expected output or expected behavior. Build it from:

• A few hand-written seeds covering the core paths.
• Edge and adversarial cases (synthetic generation helps here — see synthetic data generation with LLMs).
• Real production failures, which are your best cases once you have traffic.

The dataset is not static — it grows every time you catch a new failure. Design for append from day one.

Part 2: The metrics

Two kinds, and you want both (see deterministic vs. LLM-judge metrics):

• Deterministic — exact match, schema validation, regex, structural checks. Free, instant, reproducible.
• LLM-as-a-judge — for open-ended quality (faithfulness, helpfulness, safety) where no rule fits. (See LLM-as-a-judge.)

Each metric is a function: (input, output, expected?) → score. Keep them small and composable so a case can carry several.

Part 3: The runner

The runner iterates the dataset, invokes your app on each input, applies the metrics to each output, and collects the scores. Requirements that bite later if you skip them:

• Concurrency — real datasets are big; serial runs are painful.
• Isolation — a crash on one case shouldn't sink the run.
• Determinism controls — pin model versions and temperature so a re-run measures your change, not model drift.

Part 4: The results store

A score you can't compare is useless. You need to persist results tied to the version that produced them — which prompt, which model — so you can answer "did this change help?" (See why A/B test LLM prompts.) This is the part most homegrown harnesses under-build, and it's the part that makes evaluation actually inform decisions.

The parts that are hard to build

The four parts above get you an offline harness in a weekend. Production value comes from the parts that are genuinely hard:

• Trace integration — scoring the real execution (retrieval, tool calls, the whole path), not a re-run in a test rig. (See what is LLM observability.)
• Production scoring — running the same metrics on live traffic, not just the test set, so you catch what the dataset missed. (See run LLM evals on production traces.)
• The feedback loop — turning production failures back into dataset cases automatically.

Build vs. buy — honestly

Build the framework if evaluation is your product or you need something no tool offers. For most teams, the harness is not the product — and a homegrown one tends to grow the exact features (traces, versioned comparisons, production scoring) that already exist off the shelf, at the cost of the engineering you meant to spend on your app.

How Currai fits

Currai is the four parts plus the hard ones, already built: datasets that grow from production failures, deterministic and LLM-judge metrics, a runner, and a results store that ties every score to the prompt and model version that produced it — scoring your real traces, offline and in production, with one rubric across both. See the LLM evaluation playbook and traces and evals in one place, or start with Currai free.

Frequently asked questions

What are the parts of an LLM evaluation framework?

Four: a dataset of test cases, metrics (deterministic and LLM-judge), a runner that scores outputs against them, and a results store that keeps scores tied to the version that produced them so you can compare over time.

Should I build my own eval framework?

Build the core to understand evaluation, or if evaluation is your product. For most teams the harness isn't the product, and a homegrown one tends to grow the hard features — trace integration, production scoring — that already exist off the shelf.

What's the hardest part to build?

Not the offline harness — that's quick. The hard parts are scoring real production traces (not test-rig re-runs), running the same metrics on live traffic, and looping production failures back into the dataset automatically.

How big should my eval dataset be?

Start small — a few dozen seed cases covering core paths and edge cases — and grow it from real production failures. A dataset that grows from traffic beats a large synthetic one that never changes.