LangChain MasterClass- OpenAI LLAMA 2 LLM AI Apps|| Gen AI Course Syllabus

A definitive LangChain roadmap that equips developers to thoughtfully design, build, and deploy real-world LLM applications using the latest tools and models. This course spans over 10 modules, totaling approximately 10 hours of hands-on content, guiding you from setup to deployment with practical projects throughout. Each module builds on the last, integrating LangChain components with OpenAI and LLaMA 2 to create intelligent, production-ready AI applications.

Module 1: Introduction to LangChain & Setup

Estimated time: 0.5 hours

• Overview of LLM app development lifecycle
• Setting up Python environment and dependencies
• Configuring API keys for OpenAI and Hugging Face
• Introduction to LangChain architecture and core concepts

Module 2: Core Components & First Chain

Estimated time: 0.75 hours

• Using LLM wrappers with OpenAI and LLaMA 2
• Creating and formatting prompt templates
• Chaining components together for basic workflows
• Building your first LangChain pipeline

Module 3: Memory & Conversational Context

Estimated time: 1 hour

• Implementing memory in chains using buffer storage
• Using conversation summary memory for long-term context
• Managing session state in chat applications
• Building stateful chatbots with persistent memory

Module 4: Embeddings & Vector Retrieval

Estimated time: 1 hour

• Generating text embeddings with OpenAI and open-source models
• Working with vector databases: Pinecone and FAISS
• Setting up retrieval-augmented generation (RAG) pipelines
• Connecting retrievers to LangChain chains

Module 5: Agents & Tool Integration

Estimated time: 1.25 hours

• Understanding LangChain agents and decision loops
• Integrating tools: Python REPL, calculator, and Google Search
• Calling external APIs from agents
• Building autonomous agents with dynamic tool selection

Module 6: Project-Based App Builds

Estimated time: 1 hour

• Developing a Q&A system over custom documents
• Creating a kid-friendly category classification app
• Building a marketing copy generator with templates
• Designing a script generator and MCQ quiz builder

Module 7: CSV & Invoice Tools

Estimated time: 0.75 hours

• Parsing structured data from CSV files using LLMs
• Extracting key fields from invoices with LangChain
• Automating data entry workflows

Module 8: Ticket Classification & HR Screening

Estimated time: 1 hour

• Building a support ticket classifier with LangChain
• Automating resume screening for HR processes
• Implementing text classification pipelines with LLMs

Module 9: Email & Pipeline Automation Tools

Estimated time: 1 hour

• Generating personalized emails using LLaMA 2
• Automating bulk email campaigns
• Chaining multiple LLM steps for workflow automation

Module 10: Front-End Integration & Deployment

Estimated time: 0.75 hours

• Building web interfaces with Streamlit
• Deploying apps on Hugging Face Spaces
• Adding authentication and UI elements to AI apps

Prerequisites

• Basic proficiency in Python programming
• Familiarity with Jupyter Notebooks or IDEs
• Access to OpenAI and Hugging Face API keys

What You'll Be Able to Do After

• Build and deploy production-ready LLM applications using LangChain
• Integrate memory, retrieval, and agents into intelligent workflows
• Create real-world tools like chatbots, document parsers, and classifiers
• Deploy AI apps with Streamlit and Hugging Face Spaces
• Leverage LLaMA 2 and OpenAI models in scalable AI pipelines