Model Selection Framework

Ad hoc model selection — choosing a model because it topped a general leaderboard, because a vendor provided an effective demo, or because it was the only option known to the evaluating engineer — is a pervasive anti-pattern in enterprise AI programs. General benchmarks measure average performance across a wide distribution of tasks, but enterprise use cases are almost never average. A model that ranks third on general coding benchmarks may rank first on the specific type of code transformation that a DevOps team needs, and ranking first on a legal reasoning benchmark does not mean a model handles the particular jurisdiction and document types that a compliance team processes. The fundamental premise of a model selection framework is that selection must be grounded in task-specific evidence.

A rigorous framework proceeds through four stages. The requirements stage defines the technical requirements — latency SLA, context window length, output format constraints — and the governance requirements — data residency, SOC 2 certification, model explainability — that any candidate model must satisfy as prerequisites. The candidate shortlisting stage applies these requirements as filters to identify a tractable set of models for evaluation. The evaluation stage runs each candidate against a representative test suite composed of real enterprise examples with gold-standard labels, measuring accuracy, consistency, safety, and cost per task. The selection and documentation stage records the evaluation results, the winning model, the rationale for selection, and the conditions under which the selection should be revisited.

Documentation is a frequently underemphasized component. When the selected model is replaced eighteen months later because a superior alternative emerges, the team responsible for the replacement will be far more effective if they inherit a clear record of what was evaluated, what criteria were applied, and what the baseline performance was at original selection time. Model selection frameworks that produce living evaluation artifacts rather than one-time decision memos create compounding organizational value.