Deciding When Fine-Tuning Is the Right Tool
Explore the key factors that determine when fine-tuning an AI model is appropriate. Learn to identify conditions that justify retraining, understand its limitations and costs, and avoid common engineering mistakes. This lesson helps you decide if fine-tuning suits your AI feature or if other capabilities like retrieval or tool calling should be used first.
Fine-tuning has come up in this chapter as the fourth capability every time, and been set aside every time, because it deserves a decision made deliberately rather than folded into a comparison table. This lesson is that decision. It covers when fine-tuning is actually justified, what it costs to choose, and why the answer is “not yet” far more often than it’s “yes.” In this lesson, we will cover:
What fine-tuning actually does, in plain terms
The three conditions that actually justify it
What it doesn’t solve, even though it can look like it might
The real cost of choosing it, beyond the training itself
Where this guidance comes from beyond this course
The three ways engineers get this wrong ...