There is no “best model”
There is only the best qualified model for a defined role under stated conditions. A model that writes fluent prose may be poor at abstaining when the evidence is thin. A smaller model may beat a larger one at a tightly specified extraction task. So evaluation begins by naming the role — summarization, extraction, drafting, review — not by chasing a single leaderboard.
Why isn’t one good demo enough?
Because a single unseeded run blends four things you cannot tell apart: the model’s real capability, the luck of that particular sample, random variation in the model’s output, and the transient state of the server. Near a decision threshold, that blend is worthless. Ten successes out of ten can look like 100% and still be weak evidence for a 98% reliability claim.
Verify what actually answered
An endpoint name, a port number, a config label — these are claims, not facts. Shared serving lanes get swapped and misconfigured. Before attributing any result to a model, the honest practice is to query the serving endpoint and confirm the model that actually answered is the one you think you tested. If identity can’t be established, the correct conclusion is “unknown model,” not “Model B scored well.”
Separate a broken server from a weak model
Timeouts and connection errors measure your infrastructure, not the model’s reasoning. Score them as model failures and you slander a good model; hide them and you ship an unreliable one. Keep two numbers: quality measured only over completed responses, and an infrastructure error rate reported alongside it.
What a decision-grade evaluation records
Every run should carry its provenance: the verified model identity, the endpoint, the code version, hashes of the fixtures and prompt and output schema, the seed, and — importantly — the sampling parameters the server actually received, which middleware can quietly change. Runs should be seeded and repeated with varied seeds, pooled, and reported with confidence intervals. A promotion gate should reject a test whose sample is too small to distinguish the threshold it claims to clear, even when the point estimate looks perfect.
Why this is a service, not a footnote
The open-model landscape changes monthly, and most published benchmarks say little about legal work. A firm that adopts models on the strength of demos will eventually adopt a bad one at the worst possible moment. Rigorous, reproducible evaluation — with honest provenance and a staged, reversible promotion path from shadow mode to default — is how a firm chooses models it can defend. It is also how PLI keeps itself honest in public: claims about what works are testable by anyone with a graphics card.
This article is general information from a technology consultancy, not legal advice, and does not create an attorney-client relationship. Figures describing the founder’s own practice are illustrative, not a promise of results.