Course Description:

This course is a comprehensive, hands-on program designed to teach developers and technical professionals how to build intelligent chatbots from scratch. It combines foundational knowledge with real-world implementation using rule-based systems and AI/NLP-driven platforms. By the end of this course, participants will have designed, built, integrated, and deployed fully functional chatbots with contextual awareness, backend connectivity, and cross-platform availability.

Intended Audience:

• Software Developers and Engineers
• SDETs and QA Engineers interested in conversational testing
• Technical Product Owners and UX Designers creating chatbot solutions
• DevOps Engineers responsible for deploying chatbot-based services

Prerequisites:

• Basic programming knowledge (Python or JavaScript)
• Understanding of RESTful APIs and webhooks
• Experience with JSON, HTTP requests/responses

Key Course Takeaways:

• Understand core concepts of chatbot development, conversational design, and natural language understanding (NLU)
• Build rule-based and AI-driven chatbots using platforms like Dialogflow, Microsoft Bot Framework, and Rasa
• Implement custom actions, slot-filling, context tracking, and API integrations
• Deploy chatbots on websites, Slack, or cloud platforms like Google Cloud and Azure
• Test chatbot logic, capture analytics, and troubleshoot bot behavior in real-time

🔹 Day 1: Foundations of Conversational Bots and Rule-Based Development

Module 1.1: Introduction to Chatbots and Conversational AI

Topics Covered:

• What are chatbots? Key use cases across industries (customer service, HR, retail, etc.)
• Overview of chatbot types: rule-based, AI-driven, hybrid
• Core components: intent, entity, dialog flow, context, response
• Chatbot architecture patterns: client-channel, NLP layer, back-end service

Outcome: Understand the business value of chatbots and their underlying technical structure.

Module 1.2: Planning a Conversational Flow

Topics Covered:

• Best practices in conversation design: user-centric flows, minimal friction
• Mapping intents to user goals
• Entity recognition and slot collection
• Handling fallbacks, re-prompts, and clarifying questions
• Tools: Lucidchart, BotMock, Miro for conversation design

Outcome: Participants create a conversation blueprint for their own chatbot project.

Module 1.3: Building a Rule-Based Chatbot using Dialogflow or Microsoft Bot Framework

Topics Covered:

• Setting up a chatbot project
• Creating and training intents
• Defining entity types (system and custom)
• Creating static and conditional responses
• Using parameters and slot-filling logic
• Web chat integration using iframe or REST interface

Outcome: Build a working rule-based chatbot that responds to common queries.

Hands-On Labs:

• Lab 1: Create a chatbot in Dialogflow and define at least 5 unique intents
• Lab 2: Design and implement a multi-turn conversation using slot-filling and prompts
• Lab 3: Integrate the chatbot into a basic website using web widget or iframe

🔹 Day 2: NLP-Driven Bots with Python and Rasa Framework

Module 2.1: Introduction to NLP and Intent Classification

Topics Covered:

• Key NLP techniques in chatbot development
• Intent detection vs. entity extraction
• Preprocessing: tokenization, lemmatization, stopword removal
• Tools overview: spaCy, NLTK, Hugging Face, TensorFlow

Outcome: Participants understand how NLP models drive conversational intelligence.

Module 2.2: Building Contextual Chatbots Using Rasa

Topics Covered:

• Rasa architecture: NLU, Core, Actions
• Configuring training data with nlu.yml and stories.yml
• Defining the domain: intents, slots, entities, responses
• Training and testing the dialogue model
• Creating custom Python actions

Outcome: Build a context-aware AI chatbot that adapts based on conversation history.

Module 2.3: Managing State and Complex Dialogs

Topics Covered:

• Slot filling and conditional branching
• Form actions and validation rules
• Session persistence and context tracking
• Handling interruption and resume scenarios

Outcome: Design bots that support complex, multi-turn interactions with memory.

Hands-On Labs:

• Lab 4: Train a Rasa NLU model with custom intents and entities
• Lab 5: Define a domain file and implement slot-based form handling
• Lab 6: Write a custom Python action that calls a third-party API and returns dynamic content (e.g., weather, currency conversion)

🔹 Day 3: Integration, Deployment, and Maintenance

Module 3.1: Backend Integration and API Connectivity

Topics Covered:

• Sending data to and from REST APIs
• Using webhooks for real-time dynamic responses
• Managing user profiles in databases (e.g., MongoDB or SQLite)
• Implementing user authentication and sessions

Outcome: Learn to build bots that interact with business logic and external services.

Module 3.2: Deployment on Cloud and Messaging Platforms

Topics Covered:

• Packaging and deploying bots with Docker
• Deploying to Google Cloud Run, Heroku, or Azure Functions
• Using ngrok for local-to-cloud testing
• Connecting bots to Slack, Microsoft Teams, WhatsApp, or Telegram

Outcome: Deploy your chatbot on real channels with webhook-based event handling.

Module 3.3: Testing, Logging, and Bot Analytics

Topics Covered:

• Testing frameworks for bots: Botium, Rasa test stories
• Logging user interactions and error tracking
• Setting up usage analytics and metrics (e.g., Google Analytics, Rasa X)
• Strategies for improving bot accuracy (feedback loops, re-training)
• Human fallback and escalation handling

Outcome: Learn to monitor, test, and improve bot performance in production.

Hands-On Labs:

• Lab 7: Deploy chatbot to Slack and validate message flow
• Lab 8: Integrate chatbot with a cloud function or API for dynamic responses
• Lab 9: Configure logging and analytics, and test regression scenarios using sample scripts