Description

Introduction
In today’s dynamic digital landscape, AI agents are evolving from simple rule-based bots to intelligent systems capable of holding fluid, context-aware, multi-turn conversations. This course equips developers and AI enthusiasts with the skills and knowledge to build these advanced conversational agents. Participants will learn how to design AI-driven chatbots that can track context, personalize interactions, manage complex dialogues, and even solve problems through multi-step reasoning. Whether you’re working in customer support, HR, internal help desks, or digital commerce, mastering multi-turn dialogue systems will give you a competitive edge in delivering human-like experiences at scale.

Prerequisites

• Basic understanding of Natural Language Processing (NLP)

• Familiarity with chatbot development frameworks (Rasa, Dialogflow, etc.)

• Proficiency in Python and REST APIs

• Awareness of LLMs like ChatGPT, Claude, or open-source models such as LLaMA or Mistral

• Ideal for developers, conversation designers, and AI product teams

Table of Contents

1. Foundations of Multi-Turn Conversation Design
1.1 Dialogue Management and State Tracking
1.2 Maintaining Session Context and Long-Term Memory
1.3 Multi-Turn Use Cases and Interaction Patterns
1.4 Dialogue Policy and Flow Planning

2. Building Intelligent AI Agents
2.1 Architecting Goal-Oriented Agents
2.2 Intent Recognition and Entity Extraction for Flow Navigation
2.3 Slot Filling and Variable Tracking
2.4 Integrating Knowledge Bases and APIs
2.5 Chaining Actions and Reasoning Tasks

3. Context Management and Personalization
3.1 Session Persistence and User Context Memory
3.2 Customizing Flows Based on User History
3.3 Multi-User Conversations and Role Recognition
3.4 Personalizing Responses with Dynamic Variables

4. Tools, Frameworks, and Platforms
4.1 Using Rasa with Custom Policies and Actions
4.2 Dialogflow CX for Complex Flow Mapping
4.3 LangChain and Agentic Design with LLMs
4.4 OpenAI Function Calling and Tool Use
4.5 Combining LLMs with Traditional NLP Pipelines

5. Advanced Dialogue Patterns
5.1 Handling Ambiguity and Clarification Loops
5.2 Multi-Step Problem Solving and Chained Intents
5.3 Managing Interruptions and Return-to-Topic Scenarios
5.4 Escalation to Humans and Human-in-the-Loop Design

6. Applications and Real-World Use Cases
6.1 Customer Support and Self-Service Automation
6.2 Employee Onboarding and HR Chatbots
6.3 Conversational Commerce and Guided Sales
6.4 Healthcare Assistants and Mental Health Support
6.5 Workflow Orchestration and Task Automation Agents

7. Testing, Debugging, and Optimization
7.1 Simulating User Interactions and Dialogue Flows
7.2 Automated Testing for Flow Logic and Fallbacks
7.3 Analytics and Conversation Performance Metrics
7.4 Continuous Improvement with Feedback Loops

8. Ethics, Safety, and Responsible Deployment
8.1 Ensuring Transparency and Disclosure
8.2 Preventing Hallucinations and Misinformation
8.3 Bias Detection in Multi-Turn Responses
8.4 Guardrails for Safe and Inclusive Conversations

As conversational AI moves toward more autonomous, context-rich interactions, the ability to build advanced, multi-turn dialogue agents becomes essential. This course gives you the frameworks, tools, and best practices needed to construct AI chatbots that not only respond intelligently but also engage in meaningful conversations. By mastering these skills, you’ll be able to deliver impactful, scalable, and responsible conversational experiences across industries and domains.