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
  1. Choose documents from the questions you need answered
  2. Establish ownership and validity
  3. Check extractable content and access together
  4. Verify readiness with answer tests
  5. A worked business example
  6. Failure conditions to check
  7. A checklist for your workflow
Server storage equipment in a data center
Photo: Domaintechnik / Unsplash

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.

Question and evidence → Versions and access → Test the answer
A review sequence for the example, not a product screenshot or measured result.

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.