Mar 29 • Lets chat
How Chatbots Actually Work: From User Message to AI Response
I have previously conducted lectures on LLM orchestration, RAG pipeline, multi-modal models, and multi-agent architecture.
I am going to explain how to implement chatbot functionality by utilizing the previous lecture.
A chatbot MVP is essentially:
A system that takes a user message → understands it → optionally looks things up → generates a response → returns it
You can express this as a simple loop:
The 5 Core Components of a Chatbot MVP
Break the system into 5 understandable parts:
① User Interface (UI)
Chat screen (web, app, Slack, etc.)
Where users type messages
② Backend Controller (Orchestrator)
The “brain” that decides what to do next Routes requests between components
Connect to your previous lectures:
This is where **LLM orchestration logic** lives.
③ Large Language Model (LLM)
Generates responses
Understands natural language
④ Knowledge / Data Layer (Optional but critical for MVP+)
Documents, database, APIs
Used in **RAG (Retrieval-Augmented Generation)**
⑤ Memory (Optional but powerful)
Conversation history
User preferences
User
↓
UI
↓
Orchestrator
├── LLM
└── Knowledge Base (RAG)
↓
Response
contact information: telegram:@kingsudo7
whatsapp:+81 80-2650-2313
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How Chatbots Actually Work: From User Message to AI Response
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