When enterprises want to customize an LLM for their specific domain, two approaches dominate: Retrieval-Augmented Generation (RAG) and fine-tuning. Both improve model performance on domain-specific tasks, but they work differently, cost differently, and solve different problems. Choosing the wrong approach wastes months of engineering time.
What RAG Does
RAG adds a retrieval step before generation. When a user asks a question, the system first searches a vector database of your documents to find the most relevant passages, then passes those passages to the LLM as context alongside the question. The LLM generates an answer grounded in your specific documents — not just its training data.
RAG is ideal when: your knowledge base changes frequently (new documents, updated policies), you need citations and source attribution, you have a large corpus of documents that won't fit in a context window, or you need to update the knowledge base without retraining.
What Fine-Tuning Does
Fine-tuning trains the model's weights on your specific data — teaching it your terminology, writing style, domain knowledge, and task format. The result is a model that "thinks" in your domain without needing retrieval at inference time.
Fine-tuning is ideal when: you need the model to adopt a specific tone or writing style, you're training on structured input-output pairs (e.g., customer service responses), your knowledge is stable and doesn't change often, or you need lower latency (no retrieval step).
The Hybrid Approach
The most powerful enterprise AI systems combine both. Fine-tune the model on your domain terminology, task format, and writing style — then add RAG to ground its answers in your current documents. This gives you the behavioral consistency of fine-tuning with the knowledge freshness of RAG.
Cost Comparison
RAG: $5,000–$30,000 to build the pipeline and vector database, $0.01–$0.10 per query in API costs. Fine-tuning: $10,000–$100,000 in engineering time plus $1,000–$20,000 in compute costs for training, then lower per-query costs if self-hosted.
Decision Framework
Start with RAG for most enterprise use cases — it's faster to implement, easier to update, and provides source citations that build user trust. Add fine-tuning when you need consistent behavioral changes (tone, format, domain terminology) that RAG alone can't achieve.
