For most organizations, RAG is the right place to start. It retrieves the latest documents at the moment of the question, cites a source for every answer and enforces per-user permissions. Fine-tuning changes the model's behavior: style, format and terminology. It does not add factual knowledge that you can trust and update easily. When you need both, combine them: RAG for knowledge, and precise instructions or fine-tuning for the shape of the answer.
The difference in two minutes
RAG does not change the model. Before every answer, the system searches the organization's knowledge base for the relevant passages, and the model answers only from them and cites where they came from.
Fine-tuning is additional training of an existing model on a set of examples, each a question paired with the desired answer. It changes the model itself, so it affects the style and shape of the answer, but the knowledge it absorbs is frozen at the moment of training.
Comparison table
| RAG | Fine-tuning | |
|---|---|---|
| What changes | The information the model receives for each question | The model itself |
| Updating knowledge | Update the index, without touching the model | Retraining |
| Source for the answer | Yes, for every answer | No |
| Per-user permissions | Yes, per document and per user | No, whatever goes into the model is available to everyone |
| What you need to start | Data sources and high-quality indexing | A set of labeled question-and-answer examples |
| Best suited for | Policies, documents, products and information that changes | Fixed formats, style, professional terminology and narrow, repetitive tasks |
Why fine-tuning does not "teach" the model your policies
Training on documents does not turn them into a store the model retrieves from. The model learns patterns, and may write a convincing answer that mixes versions or invents a detail. There is no source to check, and no way to restrict sensitive information to specific employees. And when a policy is updated, you have to retrain.
When to combine them
- When the answer must come out in a fixed format, such as a report or a summary with a consistent structure.
- When requests must be classified into the organization's categories with high consistency.
- When a narrow, repetitive task can run on a smaller, cheaper model.
Before fine-tuning, try precise instructions and examples inside the prompt. In many cases, that is enough.
Examples from our projects
Israel Aerospace Industries (IAI) · AICare: RAG over aircraft technical literature, citing chapter and page, with permission-based filtering.
Allot: a knowledge assistant for service teams in Salesforce that makes recommendations based on the case context, within the system's permissions.
Ashtrom Group: an entire website turned into a knowledge base you can query in natural language and by voice.
Limitations and considerations
- RAG is only as good as its indexing: scanned documents, tables and images need processing before they go in.
- RAG adds a retrieval step to every question, and the index must be maintained as sources change.
- Fine-tuning requires labeled data and maintenance, plus the cost of training and hosting a custom model.
Frequently asked questions
Can we train a model on all our documents instead of using RAG?
Technically yes, but it is usually not a good idea. The knowledge will not stay current, answers will have no source, and you cannot restrict it by permissions. RAG solves all three.
With RAG, is our data sent to OpenAI?
No. Zooz-tech builds on Azure OpenAI and Azure AI Search in the organization's own Azure environment, and Microsoft does not use this data to train models.
Does RAG work in Hebrew?
Yes, when the indexing handles Hebrew prefixes, synonyms and mixed Hebrew and English. We cover this in detail in the guide What is RAG.
How long does it take to set up RAG?
It depends on the number of sources and their condition. Scanned documents, tables and images take more work. Usually you start with one source and a list of real user questions.
CTO at Zooz-tech. Leads development and architecture of AI agents, Generative UI and Azure infrastructure.