Deterministic vs. LLM-judge metrics: when to use which
Not every eval needs a model to grade it. Here's how to decide between deterministic metrics you can trust blindly and LLM-as-a-judge scoring you have to calibrate — and why the best suites use both.
TL;DR: Deterministic metrics (exact match, schema validation, regex, numeric tolerance) are cheap, instant, and perfectly reproducible — use them whenever the correct answer is checkable by rule. LLM-as-a-judge handles the open-ended cases a rule can't, but costs money, adds latency, and needs calibration. A good eval suite reaches for deterministic first and falls back to a judge only when it must.
There's a reflex in LLM evaluation to reach for an LLM judge for everything. It's the wrong default. A judge is the expensive, noisy tool you use when nothing cheaper works — and a surprising amount of what you want to evaluate has a cheaper answer. This guide is about drawing that line.
What deterministic metrics do well
A deterministic metric is a rule that returns the same score every time for the same input. Examples:
- Exact / normalized match — the output equals the expected answer.
- Schema / JSON validation — the output parses and matches a required shape.
- Regex / substring — a required token, format, or citation is present.
- Numeric tolerance — a computed value is within range.
- Structural checks — the right tool was called, with the right argument keys.
They're free, instant, reproducible, and impossible to argue with. Where your task has a checkable correct answer, this is the metric — no judge required.
What only an LLM judge can do
Deterministic rules fall apart the moment "correct" is a judgment: is this summary faithful? Is this reply helpful and on-tone? Is this answer grounded in the retrieved context? There's no golden string to match, so you need LLM-as-a-judge against a rubric. That buys you open-ended scoring at the cost of money, latency, and the need to calibrate the judge against humans.
The decision
| If the correct answer is… | Use |
|---|---|
| A specific value or format | Deterministic (exact match, schema, regex) |
| A required action or tool call | Deterministic (structural check) |
| Within a numeric range | Deterministic (tolerance) |
| A quality judgment (faithful, helpful, safe) | LLM-as-a-judge |
| Grounded in a source | LLM judge with the source provided |
The rule of thumb: if a rule can decide it, a rule should decide it. Spend the judge only where no rule can.
Why the best suites use both
Real apps mix both in one suite. A RAG answer might get a deterministic check that its citations exist and a judge score for whether it actually used them faithfully. An agent step gets a structural check that the right tool was called and a judge score on whether the overall task completed. (See RAG evaluation metrics and the AI agent evaluation guide.) Layering them means you catch the cheap failures cheaply and reserve the judge for what genuinely needs it — which also keeps eval cost down when you run on every production trace. (See tracking token cost.)
How Currai fits
Currai lets you attach both kinds of metric to the same traces — deterministic checks and LLM-judge rubrics — and run them together offline and on live production traffic. Deterministic results are exact and free; judge results carry their reasoning and sit next to the human labels you calibrate them with. You get one score sheet per trace with the right tool used for each dimension. See the LLM evaluation playbook and traces and evals in one place, or start with Currai free.
Frequently asked questions
What is a deterministic evaluation metric?
A rule-based check that returns the same score every time for the same input — exact match, schema validation, regex, numeric tolerance, or a structural check like "was the right tool called." It's free, instant, and fully reproducible.
When should I use an LLM judge instead?
When "correct" is a judgment with no golden answer to match — faithfulness, helpfulness, tone, groundedness. Those can't be captured by a rule, so you score them with an LLM-as-a-judge against a rubric.
Isn't an LLM judge more accurate?
Not for things a rule can decide. For a checkable answer, a deterministic metric is more accurate and cheaper, because the judge just adds noise and cost. Use the judge only where no rule fits.
Can I use both on the same output?
Yes, and you should. Deterministic checks catch structural and factual failures cheaply; judge scores cover the open-ended quality dimensions. Most robust suites attach both to each trace.