When Not to Fine-Tune: A Practical Architecture Decision Guide
A framework to decide between prompting, retrieval, and fine-tuning based on latency, governance requirements, and cost constraints.
StrategyCostModeling
Most teams overestimate the value of fine-tuning for business workflows that are primarily context-completeness problems.
Before model training, evaluate whether retrieval quality, process constraints, and response validation can solve the core reliability issue.
Fine-tuning is justified when failure patterns are stable and cannot be addressed through instruction, retrieval, or orchestration layers.