Retrieval augmented generation, usually called RAG, combines document retrieval with generated answers. Instead of relying only on a model's learned information, an application finds relevant material and supplies it as context for a response. This can help a team work with its own manuals, policies or product references, but retrieved text does not automatically make an answer correct.
The original RAG research paper describes combining generation with retrieved external knowledge. A business deployment adds practical concerns around document ownership, access restrictions, updates and the decisions people make from an answer.
Follow the question through the system
A user asks a question. The application checks access, searches an indexed collection, selects relevant passages and asks the model to answer using that evidence. The interface then presents the response with references the reader can inspect.
For a hypothetical equipment supplier, an employee might ask which maintenance steps apply to a particular model. The system should retrieve the correct model's current manual, not a similar machine's instructions merely because the wording is close.
Prepare the documents
Record source identifiers, owners, versions and publication status. Remove superseded files from the active collection or make their status explicit. Extract text carefully from tables and scans; a missing column heading can change the meaning of a retrieved number.
Break documents into useful passages while retaining context such as section title and document version. There is no universal passage size that suits every collection. Compare retrieval on the questions your employees actually ask before settling on an indexing configuration.
Enforce document permissions
A user should not receive restricted content because it shares a search index with public material. Apply access rules during retrieval and when opening cited documents. Avoid exposing snippets, filenames or answer summaries that disclose private records.
OWASP authorization guidance is a useful reference for permission checks. Our recommendation is to test both allowed and forbidden questions with different roles, including a user who guesses a document's title.
Make citations useful
A reference should point to the passage that supports the answer, with enough context for review. A link to the right document does not prove every statement in a paragraph is supported. Check whether the cited section actually contains the relevant condition, date or exception.
Show uncertainty when sources disagree or do not answer the question. A useful assistant can explain that evidence is missing and suggest who can resolve it. Inventing a confident policy is a worse outcome than a clear handoff.
Evaluate retrieval and answers separately
Create a small question set with expected evidence and known exclusions. First ask whether retrieval found the needed passage. Then inspect whether the answer represented it faithfully. These checks reveal different fixes: indexing changes may help a retrieval miss, while a misleading answer may require instructions or a constrained response format.
Include outdated policies, similar product names and questions outside the collection. Re-run the set after changing documents, search settings or models. Record unresolved cases instead of presenting a fluent demo as acceptance evidence.
Start with a narrow workflow
Begin with read-only assistance for a well-owned document collection. Keep consequential actions behind the existing approval process. Measure review effort and answer usefulness alongside error rates.
Read the RAG evaluation guide and document AI review workflow. Our AI solutions team can help define the source boundaries and tests for a practical first release.