Wizardry Labs
Wizardry Labs
ModelsEvaluationFine-tuningAI

Practice note · 6 min read

Evaluation before fine-tuning: make adaptation measurable

Fine-tuning is a tool, not a default. A clear evaluation loop helps teams decide whether adaptation is needed and whether it helped.

Evaluation before fine-tuning: make adaptation measurable

Start with the failure mode

Before changing a model, define the behavior that is not working. Is the system missing domain context, formatting outputs incorrectly, failing to use tools, or producing answers that cannot be grounded? Each failure suggests a different intervention.

Build a small evaluation loop

A useful loop has representative examples, a baseline, explicit criteria, and a repeatable comparison. Prompt changes, retrieval changes, model changes, and fine-tuning should be evaluated against the same problem—not only against an impressive demo.

Adapt with restraint

Wizardry Models is intended to source, adapt, evaluate, fine-tune, integrate, and deploy models for specific products. The right result may be a better prompt, a stronger retrieval pipeline, a provider change, or a fine-tuned model. The evaluation should decide.

Tools and concepts

Evaluation datasetsPrompt engineeringFine-tuningStructured outputsRAG

Claim status: Provided by founder; specific benchmarks require confirmation.

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