AI Chatbot Architecture Diagram Template

What this template is for

An AI chatbot architecture diagram shows how a conversational system turns a user message into a grounded, safe reply. This template lays out the full path: the channels a user arrives through (web, app, Slack), a dialog manager that handles intent and tracks conversation state, the LLM that generates responses, a RAG layer that grounds answers in your knowledge base, guardrails that filter unsafe output, and a session store that keeps conversation context across turns. Use it to design a production chatbot, document where safety and grounding happen, or explain why a chatbot is more than a single LLM prompt.

When to use this template

• Design a production chatbot before building it, covering channels through to storage.
• Explain to a stakeholder where the chatbot grounds answers and where it filters unsafe output.
• Document the session and dialog-state handling for a design review.
• Show where RAG plugs in so answers cite your knowledge base, not the model's training data.
• Trace a bad reply: was it intent, retrieval, the LLM, or a missing guardrail?
• Compare an intent-based chatbot against an LLM-first conversational design.

How to use it

1. 1Start with the user and the channels they connect through (web, app, Slack).
2. 2Add the dialog manager — intent/NLU plus dialog state tracking.
3. 3Add a session store beside the dialog manager to persist conversation context.
4. 4Add the LLM and a RAG layer below to generate grounded responses.
5. 5Add guardrails between the system and the reply to filter unsafe output.
6. 6Connect the flow end to end so the request path from user to reply is clear.

Quick example