Acquire and Store in RAG: Governing Your Vector Stores and Knowledge Bases

Once you’ve designed what a RAG assistant should do and which knowledge domains it should use, you still have two big questions: how do we bring content into the system safely (Acquire), and how do we store it (Store) so we can manage it over time?

Treat RAG as a knowledge governance problem, not just an AI one.

Acquire: selecting and preparing sources

In RAG, Acquire is about:

• Selecting repositories to index.
• Preparing documents for ingestion (cleaning, structuring, tagging).
• Defining filters and inclusion/exclusion criteria.

Start by inventorying candidate sources:

• Public docs, internal wikis, policy libraries.
• Ticket or case knowledge bases.
• Internal run‑books and playbooks.

For each, ask:

• Is the content relevant to the assistant’s mission?
• Who owns this repository?
• How often is it updated?
• What is its sensitivity and audience?

Then decide:

• Include Repositories that are relevant, maintained, and appropriate for the intended users.
• Exclude Repositories that are too sensitive, too messy, or out of mission scope.

You may also choose to include only certain sections or tags within a repository.

Clean, structure, and tag content

Before indexing, ensure content is:

• Cleaned Remove obviously outdated pages, duplicates, and low‑value noise. Fix broken links and formatting where possible.
• Chunked Split long documents into logical sections so retrieval can be precise, not just “here’s a 40‑page PDF.”
• Tagged Apply or refine metadata: topics, sensitivity, effective dates, departments, product versions.

These steps are crucial for search quality and for enforcing governance rules at query time.

Store: document stores and vector indexes

In RAG, you generally store:

• Source documents In a document store or content system.
• Embeddings Vector representations of chunks, stored in a vector database or index.

Governance questions for Store:

• Where do these stores live? Which environments, regions, or data centres?
• Who can access them? Which services, admins, and humans have read/write access?
• How are they protected? Apply your usual controls: encryption at rest, access logging, backup and recovery plans.

Remember: vector stores may not look like “data warehouses,” but they often contain sensitive information in encoded form.

Versioning and change management

Knowledge changes. Policies are updated, products evolve, errors are corrected. Your RAG stack must reflect this.

Design processes to:

• Track document versions Know which version of a document was indexed and when.
• Re‑index on change When a document is updated or removed, ensure the corresponding chunks and embeddings are updated or deleted.
• Audit index contents Periodically review what is in your indexes to catch stale or inappropriate content.

This is where DASUD’s “Store” and “Delete” stages meet: storage must anticipate future removal and updates.

Retention and deletion in RAG

Even if you’ve already decided retention at the document level, you need to apply it to RAG artefacts:

• Document store Follow existing document retention policies.
• Vector store Ensure that when documents expire or are removed, their vectors are removed as well.
• Logs of retrieval Decide how long to keep retrieval logs (queries and retrieved documents), especially where they might contain sensitive user inputs.

This keeps your RAG system aligned with broader data retention and privacy expectations.

Make it concrete

For one RAG assistant:

• List all repositories currently or planned to be indexed.
• For each, record owner, sensitivity, and inclusion status.
• Check whether documents are properly tagged and structured for RAG.
• Review where document stores and vector indexes live and who can access them.
• Design a simple process for re‑indexing when documents change.

By treating “Acquire” and “Store” as knowledge governance steps, your RAG system becomes a controlled interface to curated, current content—not just a clever way to rummage through everything.

If you’d like assistance or advice with your Data Governance implementation, or any other topic (Privacy, Cybersecurity, Ethics, AI and Product Management) please feel free to drop me an email here and I will endeavour to get back to you as soon as possible. Alternatively, you can reach out to me on LinkedIn and I will get back to you within the same day!