RAG Architecture Diagram
Map how retrieval-augmented generation grounds an LLM in your data with a vector database.
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A RAG architecture diagram shows how a retrieval-augmented generation system grounds an LLM in your own data. It traces the flow from a user query through an embedding model, a similarity search against a vector database, retrieval of relevant document chunks, and prompt assembly that feeds context plus the question into the LLM to produce a cited answer.
ML engineers, AI application developers, and solutions architects reach for this RAG diagram when designing chatbots over private knowledge bases, internal documentation assistants, or support copilots. It is a go-to reference for explaining retrieval-augmented generation in design docs, technical reviews, and stakeholder presentations.
Great for
- AI engineering design docs
- Technical architecture reviews
- Knowledge base chatbot planning
- Stakeholder presentations
- Onboarding new ML engineers
Frequently asked questions
What is a RAG architecture diagram?+
It is a visual map of a retrieval-augmented generation system, showing how a user query is embedded, matched against a vector database, and combined with retrieved context before being sent to an LLM for a grounded answer.
What are the components of a RAG pipeline?+
The core components are an embedding model, a vector database, a retriever, the original document chunks, a prompt assembly step, and the LLM that generates the final response.
How does RAG reduce hallucinations?+
By retrieving relevant source passages and injecting them into the prompt, RAG grounds the LLM in factual context instead of relying solely on its training data, which lowers hallucination and enables citations.
What is the difference between RAG and fine-tuning?+
RAG retrieves external knowledge at query time without changing model weights, while fine-tuning bakes new knowledge into the model. RAG is easier to update and cite, fine-tuning is better for style and behavior.
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