RAG Training Best Practices: How to Feed Your Agents the Right Knowledge

The single most important principle in RAG training:

The quality of your agent's output will never exceed the quality of the knowledge you give it.

This sounds obvious. In practice, most people ignore it. They upload everything they have, assume the AI will "figure it out," and then wonder why the agent gives inconsistent or mediocre answers.

The truth is that RAG training has a quality hierarchy. Some documents produce dramatically better results than others — not because of their format, but because of their content characteristics.

You don't need to rewrite all your documents before uploading. But 10–15 minutes of preparation per document can significantly improve how well the agent uses the knowledge.

One of the most common mistakes in RAG training is giving every agent access to every document. It feels comprehensive. In practice, it hurts performance.

Why Domain Isolation Matters

When an agent searches its knowledge base, it returns the top-N most relevant results. If the knowledge base is huge and covers many unrelated topics, irrelevant results push the actually relevant chunks out of the context window.

The Fix: Domain-Specific Training

Train each agent only on the knowledge relevant to its role.

Where Overlap Is Appropriate

Some documents are legitimately relevant to multiple agents. Upload the document to each relevant agent individually — there's no conflict. 2–3 agents per document is normal; 5+ agents is a red flag that the document might be too broad.

CEO.ai offers two ways to add RAG knowledge. They produce identical results — the choice is purely about convenience for your situation.

Uploading documents is step one. Verifying the agent actually uses them correctly is step two — and it's the step most people skip.

The 5-Question Test

After any significant RAG update (adding 3+ documents or replacing/updating existing knowledge), run these five tests:

What to Do When Tests Fail

RAG knowledge doesn't expire on a schedule. It expires when your business changes. Here are the signals to watch for:

Recursive Folder Ingestion

The CLI's addRagDir command with the --recursive flag is the fastest way to ingest large knowledge bases.

Chunk Size Strategy

The default chunk size is 2,000 characters. This works well for most content. Adjust only if you're seeing specific retrieval quality issues.

Automated Training in CI/CD

For teams that maintain documentation alongside code, you can automate RAG training as part of your deployment process.

name: Retrain AI Agents  
on:  
  push:  
    paths:  
      - 'docs/**'  
      - 'api-reference/**'  
  
jobs:  
  retrain:  
    runs-on: ubuntu-latest  
    steps:  
      - uses: actions/checkout@v4  
  
      - uses: actions/setup-node@v4  
        with:  
          node-version: '18'  
  
      - name: Install CEO.ai CLI  
        run: npm install -g @ceo-ai/cli  
  
      - name: Configure CLI  
        run: ceo configure --key ${{ secrets.CEO_API_KEY }}  
  
      - name: Retrain on documentation  
        run: ceo addRagDir ./docs --recursive --extensions md,txt  
  
      - name: Retrain on API reference  
        run: ceo addRagDir ./api-reference --recursive --extensions md,yaml,json

Category Overrides

The CLI auto-detects file categories based on extension. Override when the auto-detection doesn't match your intent:

RAG training isn't a project with an end date. It's an ongoing practice — like keeping your team's knowledge current. Here's a sustainable rhythm that keeps your agents sharp without consuming significant time.