What is RAG?

Retrieval-Augmented Generation, or RAG, is an AI approach that combines information retrieval with a language model. Instead of asking the model to answer from its training data alone, the system first retrieves relevant material from a knowledge base and provides it as context for the response.

This makes RAG useful for organisational knowledge, where answers should reflect approved documents such as policies, manuals, product guides, or service documentation.

How does RAG work?

A RAG pipeline usually has two stages. During ingestion, documents are parsed, split into smaller chunks, converted into vector representations, and stored in an index. During retrieval, a user question is converted into a similar representation and matched against the index.

The most relevant chunks are then passed to a language model along with the question. The model generates an answer using that context. Many implementations also show the source passages or documents used to support the response.

  • Ingest and parse approved documents
  • Split content into searchable chunks
  • Create embeddings and build an index
  • Retrieve relevant passages for a question
  • Generate a response grounded in the retrieved context

Why grounding matters

A general-purpose language model may produce fluent answers that are not based on your organisation’s current information. Grounding responses in retrieved documents gives users a way to verify where an answer came from.

Grounding does not remove the need for review. If the source document is outdated, incomplete, or contradictory, the response can inherit those problems. Knowledge base maintenance is therefore part of a successful RAG deployment.

The main components of a RAG platform

A practical platform includes document ingestion, a knowledge base, a search and retrieval layer, a language model, and an interface for users. Enterprise deployments also need access control, monitoring, and integration with existing applications.

Document parsing quality has a large effect on results. Tables, scanned PDFs, and documents with complex formatting need appropriate preprocessing. Access control must also be designed carefully so retrieval does not expose documents to users who should not see them.

Where RAG is useful

RAG fits knowledge-heavy workflows: internal policy assistants, product documentation search, support knowledge bases, compliance references, and service guides. It is particularly useful when the answer should cite the organisation’s own material.

RAG is less suitable when the task requires real-time actions, complex calculations, or decisions that need human accountability. In those cases, the system should be integrated into a controlled workflow rather than treated as an autonomous decision-maker.

  • Internal policy and HR knowledge assistants
  • Product documentation and manual search
  • Customer support knowledge retrieval
  • Compliance and procedure references
  • Service and operations guidance

Limitations and governance

RAG systems can retrieve the wrong passage, miss relevant context, or generate an answer that overstates what the source says. Evaluation, user feedback, and a review process are important.

Organisations should also define which documents are approved for ingestion, how often they are refreshed, who can access each knowledge base, and how responses are monitored. These governance decisions often matter more than the choice of model.

Conclusion

RAG is a practical way to make organisational knowledge searchable with AI assistance while keeping answers connected to source material. It works best when document quality, access control, and evaluation are treated as part of the solution.

You can explore the Sortie Services RAG AI Knowledge Platform to see how document ingestion, semantic search, and grounded responses fit together.