When organisations customise generative AI, two terms frequently arise: retrieval-augmented generation, or RAG, and fine-tuning. Both can improve an AI solution, but they address different problems.
Understanding the distinction can prevent unnecessary development costs and improve the reliability of the final application.
What is RAG?
RAG allows an AI application to retrieve relevant information from approved sources before generating an answer.
For example, an employee asks a question about a company policy. The application searches the organisation’s policy documents, retrieves the most relevant sections and sends that context to the language model.
RAG is particularly useful when:
- Information changes regularly
- Answers must be grounded in company documents
- Users need references or citations
- Data must remain outside the model
- Different users require access to different information
Typical applications include knowledge assistants, document search, customer-support tools, policy assistants and technical help desks.
What is fine-tuning?
Fine-tuning modifies a model using examples that demonstrate the behaviour or output style required.
It is useful when the model must consistently:
- Follow a specialised response format
- Use a particular tone or writing style
- Classify inputs into custom categories
- Produce domain-specific structured output
- Perform a narrow task demonstrated through reliable examples
Fine-tuning should not normally be used as a substitute for a current knowledge base. Updating changing facts through repeated training can be expensive and difficult to control.
Key differences
| Requirement | RAG | Fine-tuning |
|---|---|---|
| Access current information | Strong | Limited |
| Cite source documents | Strong | Weak |
| Change tone or behaviour | Limited | Strong |
| Update knowledge quickly | Easy | Requires retraining |
| Apply document permissions | Practical | Difficult |
| Require training examples | Not always | Yes |
Can both approaches be combined?
Yes. An application may use a fine-tuned model for consistent behaviour and RAG for access to current organisational knowledge.
However, combining them increases cost and complexity. Most enterprise knowledge-assistant projects should first establish a well-designed RAG system and evaluate whether fine-tuning is genuinely necessary.
Choosing the right option
Use RAG when the primary problem is, “The model needs access to our information.”
Consider fine-tuning when the problem is, “The model understands the information but does not behave or format its output consistently.”
The quality of the source documents, retrieval logic, prompts, permissions and evaluation process often matters more than choosing the most advanced model.
CRUXZ perspective: We design AI applications around the business requirement, security model and expected output—not around a fashionable technique.