AI Chatbot Development

Introduction

Building an AI chatbot is the 'hello world' of generative AI. The core loop is simple: take user message → call LLM with conversation history → stream the response back. Add memory, tools and RAG, and you have a real product.

Beginner analogy: Like building a messaging app where the other side happens to be an LLM.

Understanding the topic

Core concepts to understand:

• Maintain a conversation history (system + alternating user/assistant).
• Stream responses for a snappy UX.
• Add memory for long sessions (summarise old turns).
• Add tools / function calling for actions.
• Add RAG for company knowledge.

Syntax reference

Visual workflow / architecture:

User msg ───────► [system + history + new msg]
                              │ POST /chat
                              ▼
                          LLM (stream)
                              │
                              ▼
                       SSE / WebSocket
                              │
                              ▼
                        UI types tokens

Real-world use

ChatGPT, Claude.ai, Intercom Fin and most SaaS support bots are variations of this loop with different memory + RAG + tool layers.

Best practices

• Always stream — non-streaming feels broken.
• Cap history length; summarise older turns.
• Add a 'stop' button — users will abort long answers.

Common mistakes

• Sending the entire history every turn forever (cost balloons).
• Forgetting to handle network errors mid-stream.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. Walk through the architecture of a chatbot.
• Q2. Why stream responses?
• Q3. How do you handle long conversations?