Phase 2: Sequence Models · ~75 minutes · Python

Chatbots — Rule-Based to Neural to LLM Agents

ELIZA replied with pattern matches. DialogFlow mapped intents. GPT answered from weights. Claude runs tools and verifies. Each era solved the previous one's worst failure.

What you will learn

Implement chatbots — rule-based to neural to llm agents from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Learn
Languages: Python
Prerequisites: Question Answering Systems, Information Retrieval and Search
Time: ~75 minutes

The Problem

A user says "I want to change my flight." The system has to figure out what they want, what information is missing, how to get it, and how to complete the action. Then the user says "wait, what if I cancel instead?" and the system has to remember the context, switch tasks, and preserve state.

Conversation is hard for an ML system. The input is open-ended. The output has to be coherent over many turns. The system may need to act on the world (change a flight, charge a card). Every wrong step is visible to the user.

Chatbot architectures have cycled through four paradigms, each introduced because the previous one failed too visibly. This lesson walks them in order. The 2026 production landscape is a hybrid of the last two.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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