A general AI model knows a lot about the world and nothing about your business. To answer questions about your products, policies or customers, it needs your data. There are two main ways to provide it: retrieval-augmented generation (RAG) and fine-tuning. Choosing the right one saves months of work.
What RAG does
With RAG, your documents are split into passages and stored in a searchable index, usually a vector database. When someone asks a question, the system finds the most relevant passages and gives them to the model along with the question. The model answers using that material and can cite the sources.
Think of it as an open-book exam. The model does not memorise your data. It looks it up every time.
What fine-tuning does
Fine-tuning further trains a model on your own examples so it learns a style, format or specialist task. It changes how the model behaves rather than what it can look up.
Think of it as training an employee. After fine-tuning, the model writes in your brand voice, follows your output format or classifies your tickets the way your team does.
Side by side
Fresh data: RAG wins. Update a document and the next answer reflects it. A fine-tuned model must be retrained.
Accuracy and citations: RAG can show where each answer came from, which builds trust and makes errors easy to spot.
Access control: RAG can respect user permissions at search time. Data baked into a fine-tuned model cannot be selectively hidden.
Style and format: fine-tuning wins when you need consistent tone, structure or domain language.
Cost: RAG is usually cheaper to start and maintain. Fine-tuning needs curated training data and repeat training runs.
Which should you choose?
For most businesses, start with RAG. It suits knowledge bases, support assistants, policy lookup, sales enablement and internal search. Consider fine-tuning when a well-built RAG system still produces the wrong format or tone, or when you need a small, fast model to do one narrow task very well.
Many production systems combine both: a lightly fine-tuned model for behaviour, with RAG for facts.
Getting RAG right
Most poor RAG results come from poor data. Remove outdated and duplicate documents, structure content with clear headings and keep ownership of each source. Test with real questions from your team, measure how often answers are correct and grounded, and review failures every week during rollout.
Raamello builds secure RAG assistants on top of your documents, helpdesk and CRM. Talk to us about turning your company knowledge into accurate AI answers.