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 โ understands it โ optionally looks things up โ generates a response โ 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: โ User Interface (UI) Chat screen (web, app, Slack, etc.) Where users type messages โก Backend Controller (Orchestrator) The โbrainโ that decides what to do next Routes requests between components Connect to your previous lectures: This is where **LLM orchestration logic** lives. โข Large Language Model (LLM) Generates responses Understands natural language โฃ Knowledge / Data Layer (Optional but critical for MVP+) Documents, database, APIs Used in **RAG (Retrieval-Augmented Generation)** โค Memory (Optional but powerful) Conversation history User preferences User โ UI โ Orchestrator โโโ LLM โโโ Knowledge Base (RAG) โ Response contact information: telegram:@kingsudo7 whatsapp:+81 80-2650-2313