The best LLM evaluation uses representative production tasks, explicit success criteria, repeated runs, and operational metrics. Public benchmarks are useful for shortlisting, but model selection should be based on quality, cost, latency, and failure behavior on your own traffic.
The INFRO view: INFRO makes evaluation operationally simple by putting supported models behind one interface and returning comparable usage, latency, and cost data.
Build the dataset from reality
Sample successful, difficult, malformed, and safety-sensitive inputs from production with the required privacy process. Preserve the long tail: edge cases often separate models that look identical on clean prompts.
Version the dataset and prompt together. Otherwise a prompt improvement can be mistaken for a model improvement.
Choose graders that match the task
Use schema validation for extraction, unit tests for code, reference checks for factual retrieval, tool-completion status for agents, and human or calibrated model judging for subjective outputs. No single judge fits every task.
Audit model-judge disagreements and avoid using the same family as both candidate and sole judge. The goal is a decision aid, not a decorative score.
Measure operational quality
Record malformed responses, tool-call errors, refusal behavior, time to first token, total latency, token usage, cost, retries, and variance across repeated runs. A model that scores well but fails the output schema two percent of the time may be expensive to operate.
Compute cost per successful task. This automatically penalizes cheap models that require retries and expensive models used where they add no value.
Turn evaluation into routing policy
Define thresholds per workload and promote only candidates that clear them. Re-run the suite after model, prompt, retrieval, or tool changes and on a regular cadence because hosted behavior changes.
Use the results to create a routing matrix: default model, eligible substitutes, escalation model, and workloads where fallback is forbidden.
Where INFRO fits
Instead of maintaining a provider-specific harness, teams can loop over INFRO model IDs, keep the request contract stable, and compare results in the same ledger. The evaluation then becomes the evidence for routing and model-access policy.
Browse the model catalog, estimate scenarios with calculators, and use the quickstart to build a small replay harness. Model availability and behavior should always be validated against current documentation.
Frequently asked questions
Are public LLM benchmarks useful?
Yes, for shortlisting. They rarely represent your prompts, tools, constraints, latency requirements, or cost structure well enough to make the final production decision.
How many evaluation examples do I need?
A carefully stratified set of a few hundred real tasks is often more useful than thousands of synthetic prompts. Add cases as incidents and user corrections reveal new failure modes.
How does INFRO simplify model comparison?
INFRO lets a test harness call supported models through one API and compare request-level usage, latency, route, and cost without separate provider integrations.
