RAG vs Fine-Tuning: Which Should You Use for Your Knowledge Base?

When clients ask us to build an "AI that knows our company," the conversation always lands on this question. The short answer: almost always RAG. Here's why.

What each approach does

Retrieval-Augmented Generation (RAG): Your documents are chunked, embedded, and stored in a vector database. When a question comes in, the system retrieves the most relevant chunks and feeds them to the LLM as context. The model answers using that context.

Fine-tuning: You take a base LLM and continue training it on examples of inputs and desired outputs. The model's weights change to favor your style, format, or domain knowledge.

When RAG wins

• Your knowledge changes frequently (product docs, policies, pricing)
• You need source citations
• You want updates without retraining
• You're working with thousands or millions of documents
• Cost matters

When fine-tuning helps

• You need a very specific tone or format the base model can't reliably produce
• You're optimizing for latency on a fixed task
• You have thousands of high-quality input/output pairs

The hybrid path

In production, the best systems often use both: RAG for facts, light fine-tuning for tone. Start with RAG. Measure. Add fine-tuning only when you can point to a specific failure mode it would fix.

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