RAG in practice: when AI needs to consult your documents
RAG is a way for AI to consult information before answering. Instead of relying only on what a model learned during training, an application retrieves relevant passages and supplies them as context for generating a response.
The acronym stands for Retrieval-Augmented Generation. The idea appears in the research paper by Lewis and colleagues, a reference for understanding the combination of retrieval and generation.
When consulting documents makes sense
Imagine a team answering questions about an internal support manual. A model may explain general concepts, but it does not automatically know your organization’s approved version of a procedure.
RAG is an option when answers depend on specific, private or changing sources. The first requirement is suitable material to consult, rather than simply collecting a large number of files.
Microsoft’s RAG documentation describes grounding responses in organizational content. That does not mean every product automatically has access to your documents.
The three-stage workflow
Consider a fictional question: “Who approves a materials request that is incomplete?”. The application needs to locate the relevant rule before asking the model to phrase an answer.
| Stage | What happens in the example |
|---|---|
| Retrieve | Find procedure passages about requests |
| Add context | Send the question and retrieved passages to the model |
| Respond | Write guidance using that material |
Each stage’s quality matters. If retrieval selects an outdated version, a well-written answer can still be wrong for the current operation.
Try a manual exercise before implementation
Choose a short document you can use in an authorized environment. Set aside a question, the passage that should answer it and a reference that lets you find the passage again.
You can simulate the answering stage by giving those elements to an AI tool. This exercise does not build a search system; it tests how the model works with the context provided.
Answer the question using only the passages below.
For each instruction, identify the passage supporting it.
If the documents do not answer the question, state what information is missing.
If versions conflict, describe the conflict without choosing through assumption.
Question: [insert the question]
Passages: [identifier, title, version and content]Compare the answer with the original passage. A citation is useful only when its source actually supports the associated claim.
Distinguish consultation from training
Adding passages to the input for an answer is not the same as changing model parameters. You can update a retrieval collection without treating every document change as a new training exercise.
You also do not need complex infrastructure to begin assessing the need. First check whether real questions can be answered using clear sources and a small set of examples.
Make the limitations explicit
A system may fail to find the right passage, misinterpret an exception or answer beyond the evidence. Define missing information as a situation where the response should explain that absence.
Permissions also belong in the search design: the application should retrieve only content the user may access. An instruction to behave responsibly does not replace that system control.
Start with a useful source
Document a real procedure and prepare a few questions it should answer. Include one question whose answer is absent, so you can test behavior without evidence.
Use the process documentation guide to prepare your first source, and read what an LLM is if you are still building that conceptual foundation.
Frequently asked questions
What does RAG mean?
RAG means retrieval-augmented generation: an application retrieves relevant information and supplies it as context for a model to generate an answer.
Is RAG the same as training a model on my files?
No. Providing passages as context for an answer differs from changing model parameters through training.
Does RAG eliminate incorrect answers?
No. Retrieval may select an unsuitable source, and the model may misinterpret context. Check evidence, versions and permissions.