There is a pattern in teams building with language models that repeats so reliably it is almost a law. The demo works. Everyone is excited. The feature ships.
Someone changes the prompt to fix a complaint. Something else breaks and nobody notices for three weeks. The reason is always the same, which is that there is no evaluation, so there is no way to know whether a change made things better or worse. Braintrust is one of several platforms addressing that, and its documentation is unusually good at explaining the problem before selling the solution.
The conceptual material is the part worth reading regardless of what tool you end up with. It lays out why testing language model output is different from testing ordinary code. There is no single correct answer, the same input produces different outputs, quality is partly subjective, and the traditional pass or fail assertion does not apply. From there it builds the alternative, which is a dataset of representative inputs, one or more scoring functions producing numbers, and comparison across versions so you can see whether a change helped.
That reframing from correctness to measurement is the conceptual jump most teams have not made, and the documentation makes it clearly. Scoring is where the real difficulty lives and the documentation treats it seriously. There are deterministic scorers for things you can check exactly, such as valid JSON, presence of a required field, or an exact match on a known answer. There are heuristic scorers for similarity and overlap.
And there are model graded scorers, where you use a language model to judge output against criteria. The guidance covers all three, discusses when each is appropriate, and gives practical advice on writing grading prompts that produce consistent judgements. Getting started is quick, which matters for adoption. Wrap your existing function, supply a small dataset, define a scorer, run it, and you have a baseline and a comparison view.
A team can go from no evaluation to a working one in an afternoon, and given that the alternative is continuing to have none, lowering that barrier is the most valuable thing this documentation does. The connection between offline evaluation and production observability is handled well and is the part that separates the better tools in this space. Logging real production traffic, spotting cases where the system behaved badly, and turning those into test cases that go into your evaluation set is the loop that makes a system improve over time. The documentation explains that loop properly rather than treating logging and evaluation as separate features.
Now the criticisms. This is a commercial product and the documentation is a commercial document. Every explanation of a problem arrives adjacent to the feature that solves it. The conceptual material is good enough to be worth reading anyway, and you should read it knowing that the framing of what you need was written by someone selling it.
Model graded evaluation gets a friendlier treatment than I think it deserves. Using a language model as a judge is enormously convenient and it has real problems. Judges have position and verbosity biases, they are inconsistent across runs, they are expensive at scale, and they are often measuring something correlated with what you care about rather than the thing itself. Worst of all, a judge that shares a family with the model being tested may fail in correlated ways, agreeing with output that is wrong for reasons both models share.
The documentation mentions calibration and does not press hard enough on how easily this goes wrong. Dataset construction is the hardest part of evaluation and it gets the lightest coverage. Choosing which cases to include, ensuring they represent real usage rather than the cases you thought of, covering failure modes and edge conditions, and keeping the set current as usage shifts. This is where evaluation projects actually fail, and no tool can do it for you, and it deserves more than it gets.
The category is crowded. Several platforms and several open source projects cover overlapping ground, with different tradeoffs around self hosting, framework coupling and price. The documentation naturally does not help you compare, so you will need to do that work yourself. Pricing clarity at team scale is limited until you are far enough in to be committed, which is a common pattern in developer tooling and remains irritating.
My three point nine is for documentation that teaches a genuinely important discipline clearly and gets you running quickly, marked down for being unavoidably promotional, for going easy on the weaknesses of model graded scoring, and for skimming the dataset problem that determines whether any of this works. Read the concepts. Then choose your tool on the merits.