Why you need this
RAG (Retrieval-Augmented Generation) is how you give AI access to your own data—documents, FAQs, knowledge bases, customer records. It's the secret behind chatbots that "know" your company's info.
But most explanations are technical and overwhelming. This diagram makes it simple.
Perfect for:
- Product managers scoping AI features
- Executives deciding whether to build RAG systems
- Developers explaining RAG to stakeholders
- Students learning about modern AI architectures
- Anyone who needs a clear, jargon-free visual
What's inside
The Core Diagram
A one-page visual showing:
- User asks a question → "What's our refund policy?"
- Query gets embedded → Turned into numbers (vector)
- Search your knowledge base → Find the most relevant documents
- Retrieve top matches → Pull the 3-5 best chunks
- Add to prompt → Give AI the context it needs
- Generate answer → AI responds using your data
Key Components Labeled:
- Embedding Model — Turns text into searchable numbers
- Vector Database — Where your documents are stored
- Retrieval Step — How AI finds relevant info
- LLM (Large Language Model) — The AI that writes the final answer
Bonus: Mini Glossary
Quick definitions of the 5 terms you need to know (embedding, vector, retrieval, context, grounding).
How to use it
- Print as a poster — Hang in your office or meeting room
- Include in presentations — Explain RAG to clients or leadership
- Use in training — Onboard new team members to AI concepts
- Reference during planning — Keep the flow visible while designing systems
Why RAG matters
Without RAG, AI only knows what it was trained on (which stops at its cutoff date). With RAG, you can:
- Build chatbots that answer questions from your docs
- Give AI real-time access to databases or APIs
- Create custom assistants that know your company's policies
- Reduce hallucinations by grounding answers in real data
Want to go deeper?
This diagram is your visual anchor. For step-by-step implementation, troubleshooting, and advanced techniques:
- Embeddings & RAG (Full Guide) — How to build your own RAG system
- Glossary: Vector Database — Where embeddings are stored
- Glossary: Embeddings — How text becomes searchable numbers
License & Attribution
This resource is licensed under Creative Commons Attribution 4.0 (CC-BY). You're free to:
- Share with your team or students
- Print for workshops or classrooms
- Include in presentations (commercial or non-commercial)
Just include this attribution:
"RAG in One Diagram" by Field Guide to AI (fieldguidetoai.com) is licensed under CC BY 4.0
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