Model improvement planning guide
This guide helps prepare a bounded experiment. It is not a claim that a particular model or method will fit your task.
Faith Forge Labs ยท Reviewed October 16, 2026
Choose an observable task and specify the expected result. Note what should happen when the input is missing information or asks for an unsupported action.
A fictional example might classify invented maintenance messages into a small agreed list of categories, with an explicit review option for ambiguous requests. This is an illustrative evaluation, not a reported customer outcome.
Keep a baseline from the existing method. Separate failures caused by missing source information from failures in format, classification or application handling.
Confirm the intended use, handling and expected outputs with the responsible organization. Preserve a separate evaluation set so the comparison does not merely repeat the examples used to develop the change.
Compare the relevant change to instructions, provided information, model selection or adaptation. State the question, budget and stopping point. Do not assume that training from scratch is the default way to improve an application.
For technical background on evaluation and adaptation, see OpenAI's model optimization guidance and Hugging Face's PEFT documentation. The actual model and method still need task-specific review.
Compare correct results, important failures, review time and operating requirements. A change that helps one kind of input may need further checks on another.
Before release, test the complete application, record the configuration and define recovery and update checks. Review the working process or describe the task to Faith Forge Labs.