Preparing Your Content for a RAG System Implementation

A retrieval-augmented generation (RAG) system answers questions by first searching your documents, then giving the most relevant passages to a language model to write the answer. Its answers can only be as good as the content it retrieves. Before you implement RAG, inventory your content, remove outdated and duplicate material, assign owners, and set clear access rules. This preparation is usually where most of the real work lies.

Who This Page Is For

Teams building an internal knowledge assistant for support agents or employees, a customer-facing help bot that answers from documentation, or a product search assistant on their website. If you only want to be visible in public AI assistants, see our page on structuring pages for AI retrieval instead.

Step 1: Take an Inventory

List every source the system might use: help center articles, product manuals, policy documents, internal wikis, training slides, past support replies. For each, note format, owner, last update date and audience (internal, customers, everyone). You will likely find more material than expected, and much of it will be old.

Step 2: Clean Before You Connect

• Remove outdated content. An old refund policy retrieved by the system will produce a confident wrong answer.
• Merge duplicates. Three versions of the same procedure confuse retrieval. Keep one source of truth.
• Resolve contradictions. If two documents disagree, decide which is right and fix the other.
• Convert where needed. Scanned PDFs and slide decks often extract poorly. Text-based formats with headings work better.

Step 3: Assign Ownership

Every document in the system needs an owner who keeps it accurate. Without ownership, content decays and the assistant’s answers decay with it. Add a review date to each document.

Step 4: Decide Access Rules

Not every user should see every document. Salary policies, contracts and personal data must stay restricted. Make sure your RAG setup respects existing permissions and that sensitive material is excluded or protected. Involve your data protection lead early.

Step 5: Fill the Gaps

Collect the questions people actually ask (from support tickets, sales calls, internal chats). For each frequent question, check whether a clear document answers it. If not, write one. A RAG system cannot answer what was never written down.

Step 6: Test With Real Questions

1. Prepare 50 to 100 real questions with known correct answers.
2. Run them through the system and check both the answer and the retrieved source.
3. When answers are wrong, find out whether retrieval or content caused it.
4. Fix content first; it is often the cheaper fix.

Repeat the test after every major content change, and keep the question set growing as new topics come up. A short weekly review of questions the system could not answer is a simple way to find new gaps.

A Bonus: Public AI Visibility

The same cleanup helps your public presence. When your public help center has one clear, current answer per question, assistants like ChatGPT, Perplexity and Copilot are also more likely to find accurate information about your products. Our guide on optimizing your knowledge base for AI covers the public side.

How Keboost Helps

Keboost does not build internal RAG systems. It focuses on public AI assistants: tracking what customers ask about your brand across ChatGPT, Gemini, Claude, Perplexity and Copilot, and creating focused public content so those assistants find accurate answers. Run a free AI visibility check to see how public assistants answer questions about your products.