When enterprises want to customise an LLM for their domain, two approaches dominate the conversation: Retrieval-Augmented Generation (RAG) and fine-tuning. They solve different problems. Choosing the wrong one is an expensive mistake.
The Core Distinction
Fine-tuning changes the model's weights — you're teaching it new knowledge or a new style. RAG leaves the model unchanged and gives it access to external knowledge at inference time. Fine-tuning is a training decision. RAG is an architecture decision.
When RAG Wins
- Your knowledge base changes frequently (product docs, policies, pricing)
- You need the AI to cite specific source documents
- You operate in a regulated space where traceability is mandatory
- You want to go live in weeks, not months
- Your budget is constrained — RAG has near-zero recurring training cost
RAG is not a workaround for fine-tuning. For the majority of enterprise use cases — internal knowledge bases, document Q&A, customer support over documentation — RAG is simply the right architecture.
When Fine-Tuning Wins
- You need the model to adopt a very specific output format or tone consistently
- Your domain has highly specialised vocabulary the base model doesn't handle well (medical, legal, engineering)
- Latency is critical and you can't afford retrieval round-trips
- You have thousands of high-quality labelled examples to train on
Fine-tuning is not the answer to "the model gives wrong answers about our products." That's a RAG problem. Fine-tuning answers "we need it to always respond in a specific structured format" or "it doesn't understand our domain's terminology at all."
Cost Comparison
- RAG setup: moderate one-time cost (embedding pipeline, vector store, chunking strategy)
- RAG running cost: inference + vector search per query — scales linearly with usage
- Fine-tuning setup: high one-time cost (dataset preparation, training compute, evaluation)
- Fine-tuning running cost: lower per-query than RAG if you host your own model
For most enterprises processing under 10,000 AI queries per day, RAG on a hosted model (OpenAI, Claude) is cheaper than fine-tuning a self-hosted model once you factor in GPU infrastructure and maintenance.
The Hybrid Approach
The highest-performing enterprise AI systems often combine both. A fine-tuned model (trained for domain tone and format) paired with a RAG layer (for up-to-date knowledge) gives you the best of both. This is the architecture we use for enterprise applications where output quality is non-negotiable.
Decision Framework
- Does the knowledge change? Use RAG
- Is it about tone, format, or style? Use fine-tuning
- Do you need source citations? Use RAG
- Is vocabulary the core problem? Use fine-tuning
- Do you have less than 1,000 labelled examples? Use RAG (fine-tuning needs more data)
- Are you under time pressure? Use RAG (faster to deploy)