Before you begin
Keep the same test inputs, expected outputs, and constraints when comparing models.
Treat the model as one replaceable component inside the application.
Keep the workflow portable
A model is one component of the application, not the entire product.
You can begin with a capable general-purpose model, then compare alternatives as the workflow becomes clearer.
Evaluate models using the same
Inputs
Success criteria
Edge cases
Output format
Cost and latency constraints
Privacy and deployment requirements
A model that performs well for conversational writing may not be the best model for extraction, classification, code, voice, or a specialized business task.
Over time, reviewed production examples can support task-specific optimization or custom model development. That path should begin with a clear task and high-quality examples, not a desire to own a model for its own sake.
Keep the application’s workflow, data contract, and evals portable. That gives you leverage to change models without rebuilding the entire system.
Common mistakes
Choosing a model from reputation instead of task results.
Comparing models with different prompts or success criteria.
Building application logic that cannot move when the model changes.
Frequently asked questions
Is one model best for every task?
No. Extraction, classification, conversation, code, voice, and specialized business tasks can favor different models.
When should we consider a custom model?
After the task, data contract, review process, and evaluation are stable enough to produce high-quality reviewed examples.