Company knowledge
What to Prepare Before Connecting Company Documents to AI
Connecting documents is only the start. Without owners, valid versions, access rules, and test questions, retrieval results are difficult to assess. Begin with one workflow and a manageable document set.
In this article

Key takeaways
Retrieval-augmented generation (RAG) retrieves external information relevant to a question and supplies it to a model to generate an answer.
Create separate tests for a general refund and an exception contract.
A connected folder is not proof of operational readiness.
Connecting a folder can feel like the finish line. Yet which source wins when a refund policy conflicts with a contract exception? Preparing RAG starts with making that decision clear enough for people to defend.
For the underlying concept, see What is retrieval-augmented generation (RAG)? in the AI glossary.
Choose documents from the questions you need answered
RAG retrieves relevant material for the answer input. A fictional support team might begin with recurring refund-eligibility questions. Select approved policies and exception guidance that support those answers rather than connecting an entire shared drive.
Establish ownership and validity
A filename saying “latest” may still lack an effective date. List each document’s owner, audience, effective date, and replaced version. Conflicting authoritative sources may require a business decision before an AI configuration change.
Check extractable content and access together
Inspect extracted text, table columns, and notes. Apply access boundaries during retrieval as well as at the source. Microsoft’s security-filter pattern limits results but does not itself authenticate the supplied identity; do not trust a user-selected organization name as authorization.
Verify readiness with answer tests
Define authoritative evidence and unanswerable cases for each representative question. Test current rules, exceptions, and unauthorized requests separately. Record evidence retrieval separately from faithful answer generation to locate the next improvement.
A worked business example
Consider a support team connects refund policies. General rules and contract-specific exceptions share a folder, but ownership and effective dates are unclear.
Create separate tests for a general refund and an exception contract. Assign valid source passages and authorized users, then inspect whether retrieval preserves both the applicable clause and its conditions.
Failure conditions to check
A connected folder is not proof of operational readiness. If owners have not resolved conflicting policies, a fluent answer cannot establish the correct rule.
A checklist for your workflow
Item | Review question |
|---|---|
Scope | Does each task question map to source material? |
Owner | Who resolves conflicts and retires content? |
Access | Are retrieval results permission-scoped? |
Frequently asked questions
Are more documents always better?
Duplicates and obsolete material complicate evaluation. Define the evidence scope first.
Related AI concepts
Sources and editorial information
Anthropic: Contextual Retrieval
Microsoft: Security filter pattern
TENCONIC editorial team · Structure reviewed October 28, 2026. Official sources explain concepts and implementation examples; features vary by product. Scenarios and checks are editorial recommendations, not customer results or product promises.
For the approach to connecting company knowledge with AI, explore the TENCONIC Data Engine introduction.