RAG Explained: How to Give Your AI Agents Your Business Knowledge

Out of the box, AI models know a lot about the world but nothing about your business. They can't answer "What's our refund policy?" or "How do we onboard enterprise clients?" because that knowledge lives in your head, your docs, and your SOPs.

RAG — Retrieval-Augmented Generation — bridges that gap. It lets your AI agents search your own documents and use that context to generate accurate, business-specific answers.

How RAG Works (Simply)

1. Upload your documents — PDFs, Word docs, spreadsheets, markdown files, even website pages
2. Automatic chunking — Your documents are split into small, searchable pieces
3. Embedding — Each chunk is converted into a numerical representation that captures its meaning
4. Storage — Embeddings are stored in a vector database (like Pinecone or Qdrant)
5. Retrieval — When your agent gets a question, it searches for the most relevant chunks
6. Generation — The AI uses those chunks as context to generate an accurate answer

What to Upload

The best RAG sources are documents that your team references frequently:

• Product documentation — features, pricing, specs
• Support FAQs — common questions and answers
• SOPs — standard operating procedures for onboarding, fulfillment, etc.
• Sales materials — pitch decks, case studies, objection handlers
• Policy documents — refund policies, terms, compliance requirements

Setting Up RAG on Ideople

Ideople makes RAG simple. In the agent builder:

1. Navigate to the Knowledge tab
2. Click Add Source and upload your files
3. Choose your embedding provider (OpenAI, Cohere, Jina, Mistral, or VoyageAI)
4. Wait for indexing (usually under a minute)
5. Your agent now has access to your business knowledge

Tips for Better RAG Results

• Keep documents focused — one topic per document performs better than massive all-in-one files
• Update regularly — if your pricing changes, update the knowledge source
• Use clear formatting — headings, bullet points, and structured content retrieves better than walls of text
• Test with real questions — ask your agent questions customers actually ask and verify the answers

The Result

With RAG, your support agent doesn't just give generic answers — it gives your answers. Your sales agent knows your pricing tiers. Your onboarding agent knows your process. It's the difference between a general-purpose chatbot and a team member who's read every document in your company.