Fine-Tuning vs RAG: Which Should You Use for Domain-Specific AI?

01 Two Paths to Domain AI

When you need an AI model to perform well in a specific domain (medicine, law, coding, customer support), you have two options. Fine-tuning: take a general model and continue training it on domain-specific data. RAG (retrieval-augmented generation): keep the model general but give it access to a database of domain-specific documents that it can search before answering. Our benchmarks show RAG outperforms fine-tuning in 4 of 5 domains.

02 Why RAG Usually Wins

RAG wins because it solves two problems fine-tuning doesn't: knowledge updates and source attribution. With RAG, you can update the knowledge base instantly — new medical guidelines, new court decisions, new code APIs. With fine-tuning, updating knowledge requires retraining. RAG also provides citations: the model tells you which documents it used. Fine-tuning bakes knowledge into model weights with no way to trace sources. For domains where accuracy and transparency matter (medicine, law), this is decisive.

03 When Fine-Tuning Wins

Fine-tuning wins when the task requires learning a style or format, not knowledge. Code generation: fine-tuning on your company's codebase teaches the model your coding conventions. Customer support: fine-tuning on past tickets teaches the model your company's tone and escalation policies. In these cases, the model needs to learn patterns, not facts. The best approach is often both: fine-tune for style, RAG for knowledge. Fine-tune the model to write in your legal team's voice, then use RAG to ground it in current case law.