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RAGBOX / Knowledge

How RAG connects AI to company knowledge

RAG retrieves relevant information and supplies it as context for model-generated answers. Enterprise RAG adds access, version and audit requirements so retrieval uses knowledge the asker is allowed to access.

EP 05 · Updated 2026-09-14 · Published 2026-09-14 · Written by RAGBOX · Reviewer: awaiting assignment
Definition and overview

RAG retrieves relevant information and supplies it as context for model-generated answers. Enterprise RAG adds access, version and audit requirements so retrieval uses knowledge the asker is allowed to access.

Putting it into practice

Carry permissions into documents and chunks; enforce them before model context is built. Test answerable and forbidden questions. Update indexes and caches when sources are deleted or access changes. Prompts are not an authorization system.

Workflow

Evaluate two stages separately: did retrieval find the right document, and did generation follow it? Inspect retrieved evidence before adjusting the prompt. Include unanswerable questions and inaccessible documents to test refusal as well as successful answers.

RAGBOX Private AI Control Framework

Before you begin

  1. Chunk permissions
  2. Traceable sources
  3. Index and cache deletion

Capabilities and deployment require project-level confirmation. No unsupported certification, ROI or customer claims are made.

Key takeaways

  1. Select permitted documents
  2. Assign data owners
  3. Prepare reference questions and answers
  4. Test denied access and deletion
  5. Measure quality, speed and cost before scaling

This is RAGBOX planning guidance, not an external certification standard. Each layer needs an owner, evidence and tests.

Frequently asked questions

Does data have to leave the network?

It depends on deployment and connected services. Map model, OCR, embedding, backup and log traffic before confirming the boundary.

Can an AI answer be trusted immediately?

Check the original, completeness and version, especially for legal, accounting and consequential decisions. Citations support review but do not guarantee accuracy.

How should a project begin?

Choose one defined use case, approved documents, owners and acceptance criteria. Test answer quality, permissions and cost with a small group before scaling.

References

Public sources explain principles; they do not certify or endorse RAGBOX.

Expert review is required before operational use. This is not case-specific legal or tax advice.

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Shown capabilities are design concepts. Confirm scope and test the implementation before use. This demonstration connects to no live AI or customer data.