AI document search starts with organising the sources.
An assistant can help find information in contracts, procedures and manuals. Without distinguishing versions or permissions, it can make incorrect information easier to find. Before choosing a model, decide which documents enter the system, who can consult them and how the team recognises a well-supported answer.
Choose a manageable document collection
Start with an area that has an owner and recurring questions, such as one team's procedures. Identify each document's version, date, status and origin. Remove duplicates that should no longer be used and flag files needing review. This preparation also helps if conventional search proves sufficient for part of the problem.
Check what extraction actually reads
A visually correct PDF can produce incomplete text, incorrectly associated tables or pages with no extracted content. Choose representative samples and compare extracted text with the original. Preserve page or section references so people can verify answers. Importing a file does not establish that all its content became searchable.
Apply permissions before retrieval
Microsoft's search documentation describes document-level access controls. The principle for your solution is to retrieve only what the user is authorised to consult. Hiding a reference after its information has already informed an answer does not solve access control. Define how permission changes and deletions propagate to the index.
- An owner and status for every source.
- Groups authorised to consult the document.
- A procedure for changing or revoking access.
- Updates when a version is superseded.
- Removal of derived data when required.
Test answers and missing answers
Prepare questions requiring a particular source and others that documents cannot answer. The assistant should make that absence clear and route questions rather than fill gaps with assumptions. Compare accounts with different permissions as well. Local infrastructure may be an option, but does not remove the need for access controls, backups and collection maintenance.
Frequently asked questions
What does RAG mean?
It is an approach that retrieves source information before generating an answer. Quality depends on search, retrieved content and how the answer is constructed.
Does a Mac mini make every document private?
Running components locally is an infrastructure choice. Integrations, access, logs, backups and the actual data path also need checking.
Reference documentation
A note from ArqWeb
This guide organises checks and decisions for a common situation. Implementation depends on the website, access and systems involved. We can assess your situation if you need help applying these steps.
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