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
Every time your project produces a result, three outcomes are possible
Accepted
The result was useful as written.
Corrected
A person changed the result.
Escalated
The app did not have enough information or confidence.
Those outcomes are valuable.
A few dozen reviewed examples can reveal
Repeated failure patterns
Missing context
Rules that need clarification
Edge cases
Whether another model performs better
Whether a task-specific model is worth developing
The important word is reviewed. Raw usage is not automatically good training data.
Capture
Input
Original output
Final approved output
Reason for correction
Relevant policy or source
Reviewer
Version of the prompt and model
This creates an improvement loop grounded in the way the business actually works.
Common mistakes
Treating raw usage as clean training data.
Saving corrections without the reason or approved final output.
Training a custom model before the task and evaluation are stable.
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.