RAG Chatbot Architecture
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Dortha Franecki, Computer Science StudentWalk through the full request lifecycle of a production-ready RAG (Retrieval-Augmented Generation) chatbot — from input sanitization through vector retrieval, LLM inference, and response delivery. Designed for developers, system architects, and technical interviewers who need to communicate how a modern AI system handles context, memory, and safety in a single sequence.
How to create a RAG Chatbot Architecture
To create a RAG chatbot architecture, follow these steps:
01.
Map the layers first
Identify your core components: UI, safety/guardrails layer, backend API, session cache, vector database, and LLM. Each becomes a participant in the sequence.
02.
Start with the safety gate
Model input validation as the first step — before the backend ever sees a prompt. Use alt blocks to show the rejected vs. safe paths.
03.
Add session memory
Show the backend querying a cache (e.g., Redis) to retrieve recent conversation history before calling the LLM. This is what makes the chatbot feel coherent.
04.
Model the RAG step
Insert a vector DB query between the memory lookup and the LLM call — the backend embeds the sanitized prompt and retrieves relevant context.
05.
Build the LLM call
Pass the combination of history, retrieved context, and current prompt to the model. Show the response flowing back through the chain.
06.
Use autonumber
Add autonumber at the top of the sequence — it labels every step automatically and makes the diagram easy to reference in documentation.
07.
Use critical blocks for multi-step processing
Wrap the backend processing steps in a critical block to visually group the core request logic.
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