RAG and Fine-Tuning: Giving Models Knowledge
Most problems with a model’s answers are either missing knowledge or inconsistent behaviour. These pages cover which fix applies to which, and how retrieval relates to tools and agents.
2 articles · start with the first and read in order, or jump to what you need
- 1 · IntermediatePrompting, RAG or Fine-Tuning: Which to UseThree ways to make a model better at your task, in the order to try them — because most teams reach for the most expensive one first.7 min read
- 2 · IntermediateRAG, MCP and Agents Solve Three Different ProblemsThey get compared as if you pick one. You do not — they answer separate questions: what the model knows, what it can reach, and who decides what happens next.7 min read
Common questions: RAG & Fine-Tuning
Should I use RAG or fine-tuning?
Use retrieval (RAG) when the model lacks facts, especially private or current information. Consider fine-tuning only when a consistent style, format or behaviour cannot be held with good prompts at your volume. Try improving the prompt before either.
What is the difference between RAG and MCP?
RAG is about what a model knows: relevant documents are retrieved and placed in front of it at request time. MCP, the Model Context Protocol, is about what it can reach: a standard way to connect models to tools and data. They solve different problems and are often used together.
Can fine-tuning teach a model new facts?
It is the wrong tool for that. Facts learned in training are hard to update and hard to trace back to a source. Retrieval keeps facts in documents you can update and cite.