Guide / Industrial AI
Industrial RAG with citations and access controls
An employee asks about maintenance. The application returns a plausible answer from a manual for a different equipment revision. If the response contains only fluent prose, the mistake can be difficult to spot.
A useful industrial knowledge system must find the applicable source, respect the user's permissions and make the basis of its answer inspectable. Adding documents to a search index does not resolve those requirements.
Retrieval-augmented generation, or RAG, combines information retrieval with text generation. The original RAG paper describes that combination. The industrial operating context requires additional design around document status, identity and review.
RAG, search or fine-tuning?
Retrieval-augmented generation combines finding external information with drafting an answer. Search returns relevant sources; a language model can use them to compose a response. Fine-tuning changes model parameters using training examples. It does not replace a current document repository with enforced permissions.
Use keyword search and metadata filters for known titles or part numbers. Semantic search can help when the question and document use different wording. Combine the methods where exact identifiers and conceptual similarity both matter. Consider fine-tuning for a repeated behaviour or output requirement only after testing clear instructions and examples.
Document preparation matters too. Splitting a table from its headings or a warning from its procedure can remove essential context. Inspect the retrieved passages separately from the final answer. Compare candidate models using the same passages before deciding which component needs improvement. The deployment guide covers where that model should run.
Give each document an operational identity
Record the document version, applicable asset or product and approval status. Decide how superseded material is handled. An older service report may be informative without being the current authority for a maintenance procedure.
For example, a question about equipment A-17 needs the correct configuration. If that information is missing, the application should request it or pass the question to a responsible person. Guessing the configuration creates a false appearance of completeness.
A proposed workflow is:
Identify user → restrict eligible sources → retrieve relevant passages → prepare answer → show evidence and unresolved points
The permission boundary belongs in the application and connected systems. A prompt that asks the model to be discreet is not access control.
Make citations useful to the reviewer
Show the title, version and relevant passage for an important statement. The user should be able to open the source where their permissions allow it. A link to a broadly related document is not enough to support a particular instruction.
If sources disagree, expose the disagreement. If there is no adequate source, say so. These responses may be less satisfying than a complete-looking answer, but they preserve the information the user needs to make a decision.
Retrieved documents can also contain instructions intended to manipulate model behaviour. OWASP documents this prompt-injection risk. Treat source material as evidence to examine, not as authority to grant the application additional actions.
Check how changes reach the application
Test a new document version and a revoked permission. Both changes need to reach caches and prepared responses as well as the source system. A shared cache must not return material after a user loses access.
Set an acceptable update delay based on the task and verify it. Assign someone to maintain the connection between equipment, document revisions and approval status.
Test retrieval and answers separately
Use questions with known sources, obsolete versions, restricted documents and genuinely missing answers. First check whether retrieval finds the right evidence. Then check whether the response represents that evidence accurately.
Keep those results separate. A search failure calls for different changes from an unsupported conclusion drawn from a correct passage. Include questions that the system should decline to answer rather than judging it only on successful responses.
Before expanding the system, measure the time users spend checking and correcting its output. The practical outcome is a reliable route to usable information, not simply a high proportion of answered questions.
Welf connects these applications to existing identities and information sources. Read about enterprise integrations, or bring a bounded document collection and a set of recurring questions to the first discussion.