How to Fine-Tune an LLM: An End-to-End Guide

Fine-tuning a smaller language model can dramatically outperform even the most advanced foundation models on highly specific tasks requiring rigid formatting. For instance, one team boosted accuracy on a complex medical reporting task from a mere 35% with prompts to an impressive 98% by fine-tuning a 7B model, also eliminating high API costs. This technique is particularly effective for enforcing complex structural rules or managing intricate conditional logic, whereas Retrieval-Augmented Generation (RAG) is better for augmenting knowledge with frequently changing information. Parameter-efficient methods like LoRA make this possible by freezing the original model and only training small "adapter" matrices, which specializes the model's behavior without being computationally expensive.