LLM evaluation: measure your AI before your users do
Evaluation suites, regression testing, and production monitoring for LLM features — so you know when quality moves, and in which direction.
a prompt change cannot ship if it made anything worse
- Evaluation datasets
- Automated scoring
- Regression gates in CI
- Best when
- An AI feature is live or about to be, and nobody can say how good it is.
- Not a fit when
- You have no AI feature yet and no real usage to measure.
- First thing you get
- An evaluation set built from your real cases and a first scorecard on the current version.
What are LLM evals and observability?
LLM evals are repeatable tests that score an AI feature's output against a curated set of real cases, so a prompt or model change is judged before it ships. Observability is the production side: tracing and monitoring every model call so shifts in quality, cost and latency are visible. We set up both, wired into your release process.
Most teams ship AI features with no way to tell whether they are getting better or worse. A prompt gets tweaked, a model version rolls forward, a retrieval index is rebuilt — and quality shifts silently until somebody complains.
Evals are the missing test suite. They turn subjective quality into something you can regression-test in CI, the same way you already do with application code. It is unglamorous work and it is usually the highest-leverage thing a team with a live AI feature can invest in.
What this includes
- 01
Evaluation datasets
Curated test cases drawn from your real usage, including the edge cases that actually break things.
- 02
Automated scoring
Programmatic and model-graded scoring so quality is measured on every change, not sampled by hand.
- 03
Regression gates in CI
Prompt and model changes blocked from shipping when scores drop below your threshold.
- 04
Production tracing
Full traces of prompts, retrievals, tool calls, and outputs, so a bad answer can be reconstructed.
- 05
Cost and latency tracking
Per-feature token spend and response times, so quality decisions are made with the bill visible.
- 06
Model migration testing
Compare candidate models against your own eval set before switching, rather than trusting a benchmark.
Situations we see most often
You have an AI feature live and no idea if it is good
We build the eval set from your production traffic and give you a number to track, plus the traces to understand it.
A model version is being deprecated
Migrating models without evals is a leap of faith. With them, it is a comparison you can defend to stakeholders.
Prompt changes keep breaking something else
Regression suites catch the second-order damage a prompt tweak causes before it reaches users.
Your AI spend is unpredictable
Per-feature cost tracing shows where tokens actually go, which is usually not where teams assume.
What to know before you start
Why do AI features need evaluation at all?
Because their quality moves without anyone touching the code. A prompt gets tweaked, a model version is updated by the provider, a retrieval index is rebuilt, and answers that were right last week become subtly wrong. Without measurement, the first sign is a complaint.
Evaluation turns quality into a number you can track, so every change is judged against real cases before it ships rather than after users find the problem.
What goes into an evaluation set?
Real examples from your own usage: typical requests, the edge cases that have caused trouble, and the inputs where the right answer is to refuse or say it does not know. Each is paired with what a good output looks like, or with rules a good output must satisfy.
About fifty well-chosen cases are enough to start. The set then grows from production traffic, so it keeps reflecting what users actually ask.
What does observability add once a feature is live?
Visibility into every model call: the input, the output, the model and prompt version, latency and cost, traced through each step of a chain or agent. When something goes wrong you can see exactly what the model was given and what it returned.
Aggregated, the same data shows trends: quality drifting, latency creeping up, or one feature quietly becoming most of the AI bill.
How do you test a migration to a new model?
By running the full evaluation set against the new model before switching. The scorecard shows where it is better, where it is worse, and whether prompts need adjusting, so a forced migration, such as a deprecated model, becomes a measured decision instead of a leap of faith.
How do you keep AI costs under control with observability?
Every call is logged with its token usage and cost, so spend can be broken down by feature, customer and model. That shows where caching would pay off, which requests could go to a smaller model, and where prompts carry more context than they need.
Budgets and alerts then catch unexpected spikes, such as a loop in an agent or a sudden surge in usage, before they become a surprising invoice.
What drives the cost of LLM evals and observability?
Setting up evaluation is a modest project compared with the feature it protects. These factors decide its size.
| Factor | Effect | Why it matters |
|---|---|---|
| Number of AI features | High | Each feature needs its own evaluation set, scoring rules and dashboards. |
| Building the evaluation set | High | Collecting and labelling real cases with good expected outputs is the core effort. |
| Scoring approach | Medium | Deterministic checks are cheap; model-graded scoring for tone or helpfulness needs careful rubrics. |
| Tracing integration | Medium | Instrumenting chains, agents and retrieval steps for full traces takes more than logging single calls. |
| CI integration | Low | Adding a regression gate to your pipeline is quick once the evaluation set exists. |
What a typical engagement looks like
- 011–3 weeks
Discover
Process map
- 022–4 weeks
Design
Interactive prototype
- 03two-week cycles
Build
Working software each cycle
- 04ongoing
Ship & support
Automated pipeline
What we build it with
- Tracing
- OpenTelemetryLangfuseStructured logs
- Evaluation
- Model-graded evalsGolden datasetsCI gates
Questions we get asked
01How many test cases do we need to start?
Fewer than most teams expect. Fifty well-chosen cases covering your real failure modes are more useful than a thousand generic ones. We start there and grow the set from production traffic as patterns emerge.
02Can you add evals to an AI feature we already built?
Yes, and this is the most common way we are engaged. We instrument the existing feature, build an eval set from actual usage, and put a regression gate in front of future changes.
03Is model-graded evaluation trustworthy?
For subjective qualities like tone and helpfulness it correlates well with human judgement, provided the grading rubric is specific. We combine it with deterministic checks — format, citation presence, factual assertions — rather than relying on it alone.
04Which tools do you use for tracing and evaluation?
OpenTelemetry and Langfuse for tracing, structured logs, and our own evaluation harness wired into CI. We work with tools you already use where they fit.
05Do evals run on every change?
Yes, as a gate in CI for prompt, model and retrieval changes, plus scheduled runs against production samples so drift is caught even when nothing was deployed.
Adjacent capabilities
Further reading
Tell us what you are building.
Share the problem and we will come back with an approach, a rough shape of the work, and what it would take to deliver it.