Building Chatbots with LLMS and LangChain
Artificial Intelligence
Course Description:
This practical 3-day course empowers developers, technical professionals, and AI enthusiasts to build intelligent, context-aware chatbots using Large Language Models (LLMs) and the LangChain framework. Participants will learn how to design, implement, and deploy chatbots that go beyond simple responses by integrating memory, tools, APIs, and knowledge bases. The course covers LLM prompting, LangChain components, and deploying chatbots using local or cloud-based environments.
Duration: 3 Days
Format: Instructor-led, hands-on sessions with guided labs, live demos, and real-world use cases
Description
? Day 1: Foundations of LLMs and LangChain
Session 1: Introduction to LLMs and Modern Chatbots
- What are LLMs? (e.g., GPT-4, Claude, Mistral)
- How chatbots evolved with generative AI
- Limitations of basic LLMs and why LangChain is needed
Session 2: Getting Started with LangChain
- LangChain architecture and core components
- PromptTemplates
- Chains
- Agents
- Memory
- Setting up the environment (Python, LangChain, OpenAI API)
Session 3: Prompt Engineering for LLM-based Chatbots
- Best practices for prompt design
- Role prompting, system messages, and dynamic prompting
- Zero-shot, few-shot prompting examples in LangChain
Lab Activities:
- Set up a chatbot using OpenAI and LangChain
- Build simple chains and prompt templates
- Experiment with basic memory and prompt variations
? Day 2: Advanced Chatbot Features with LangChain
Session 1: Working with Memory, Context, and Conversation History
- LangChain memory modules (ConversationBufferMemory, SummaryMemory)
- Context retention and personalization
- Resetting and managing session flow
Session 2: Integrating Tools and External Data
- LangChain Agents and Tools
- Calling APIs from the chatbot (e.g., weather, databases, CRMs)
- Retrieving knowledge from files, documents, and URLs
Session 3: Building Knowledge-Enhanced Chatbots
- Document loaders and text splitters
- Embedding and vector stores (e.g., FAISS, Chroma)
- RAG (Retrieval Augmented Generation) with LLMs
Lab Activities:
- Build a chatbot that integrates external tools and APIs
- Create a document-aware assistant using embeddings and FAISS
- Add memory and history-aware conversation flow
? Day 3: Deployment, Customization, and Final Project
Session 1: Deploying Chatbots
- Building a frontend with Streamlit or Gradio
- Deploying on cloud (e.g., Hugging Face Spaces, Render, or local Docker)
- Securing API keys and handling errors
Session 2: Customization and Real-World Integrations
- Creating multi-modal and multi-turn assistants
- LangChain + OpenAI function calling
- Integrating with messaging platforms (e.g., Slack, Telegram, WhatsApp)
Session 3: Capstone Project + Showcase
- Project development: Use case definition, design, and build
- Presentations and demos
- Feedback and discussion on deployment strategies
Lab Activities:
- Deploy a chatbot on a live demo platform (Streamlit/Gradio)
- Customize agent routing logic for task-specific conversations
- Present a domain-specific chatbot use case (e.g., customer service, HR assistant)
