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When Fine-Tuning Makes Sense: A Developer's Guide
Most teams are curious about fine-tuning and how it might help their AI products, but they don't know what to expect from the process, how to measure success, or where to start.
This guide walks through the concrete benefits of fine-tuning, helps you identify which goals matter for your use case, and shows you how to get started with a clear path to measurable results. Add examples of common failure modes to your training set (with the correct expected output), and the model will learn to avoid these pitfalls. It will guide you through every step of the process including creating training data, fine tuning models, and evaluating fine-tunes to find the best one for your project.
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