LangChain Mastery: Build GenAI Apps with LangChain &Pinecone Course Syllabus

Overview: This project-driven course provides a comprehensive introduction to building production-ready generative AI applications using LangChain, Pinecone, and Streamlit. You'll gain hands-on experience with real-world use cases like document summarization, retrieval-augmented generation (RAG), and AI-powered chatbots. With over 4 hours of structured content, you’ll progress from environment setup to deploying interactive web interfaces—equipping you with in-demand skills for AI application development.

Module 1: LangChain & Environment Setup

Estimated time: 0.5 hours

• Install Python and required dependencies for LangChain
• Set up Pinecone and Chroma vector databases
• Configure API keys for OpenAI and Google Gemini
• Understand LangChain architecture: chains, agents, and vector workflows

Module 2: Building a Document Summarizer

Estimated time: 1 hour

• Create a document summarization system using LangChain chains
• Implement 'stuff', 'map_reduce', and 'refine' summarization strategies
• Integrate vector embeddings for processing large documents
• Perform question answering over summarized content

Module 3: RAG & Vector Stores

Estimated time: 1 hour

• Set up Pinecone for vector indexing and retrieval
• Query vector stores using Chroma
• Build Retrieval-Augmented Generation (RAG) pipelines
• Connect vector databases to LLM outputs for context-aware responses

Module 4: LangChain Agents & Chains

Estimated time: 1.25 hours

• Design multi-step agent workflows in LangChain
• Use tools, prompt templates, and function calling within agents
• Test and refine agents using Jupyter AI assistants

Module 5: Interactive Streamlit Front-End

Estimated time: 1 hour

• Build web interfaces for LLM applications using Streamlit
• Implement Streamlit widgets, session states, and callbacks
• Deploy chatbots, file uploaders, and summarizer apps

Module 6: Prompt Engineering & Best Practices

Estimated time: 0.75 hours

• Apply prompt templates and few-shot prompting techniques
• Use chain-of-thought and refinement strategies
• Troubleshoot prompt performance and context issues in real applications

Prerequisites

• Working knowledge of Python programming
• Familiarity with Jupyter notebooks and basic command-line usage
• Basic understanding of AI/ML concepts (helpful but not required)

What You'll Be Able to Do After

• Build end-to-end LLM-powered applications using LangChain
• Integrate vector databases like Pinecone and Chroma for semantic search
• Develop and deploy interactive RAG chatbots and summarizers
• Apply advanced prompt engineering techniques in production workflows
• Deploy AI applications with user-friendly Streamlit interfaces