7 Fine-Tuning LLMs for Improved Performance
This chapter covers
- Choosing among prompting, RAG, or fine-tuning for domain-specific tasks
- Preparing high-quality training data for fine-tuning
- The fine-tuning process for both closed-source and open-source models
- A complete hands-on walkthrough of building a reliable customer support assistant
- Using knowledge distillation to create smaller, faster student models for production
"This model gives okay results, but it's not precise enough for our medical terminology."
"The AI generates good text, but it doesn't understand our company's internal jargon."
"We need more accuracy for legal documents—general models make too many subtle errors."
Do these concerns sound familiar? You're not alone. While today's Large Language Models (LLMs) demonstrate impressive capabilities across a wide range of tasks, they often fall short when facing highly specialized domains or complex workflows requiring domain expertise.
In previous chapters, we discussed various techniques to optimize LLM performance, including prompting (zero-shot, few-shot) and Retrieval-Augmented Generation (RAG). These methods allow us to get better results from models without changing their underlying weights. However, there are cases where these techniques are insufficient, and more fundamental adjustments to the model’s behavior are necessary. This is where fine-tuning becomes valuable.