🔹 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)