Master LangChain & Gen AI -Build #16 AI Apps HuggingFace LLM Course Syllabus

Overview: This project-driven course guides beginners through building 16 real-world AI applications using LangChain and Generative AI frameworks. You'll progress from foundational concepts to deploying interactive AI tools, integrating multiple LLMs including OpenAI, Hugging Face, LLaMA 2, and Google Gemini. With hands-on projects spanning education, marketing, data analysis, and customer service, you’ll master RAG workflows, prompt engineering, and front-end integration using Streamlit and Hugging Face Spaces. The course spans approximately 10 hours of content, designed for developers looking to enter the GenAI space with practical, deployable skills.

Module 1: LangChain Basics & Setup

Estimated time: 0.5 hours

• Set up Python environment and required libraries
• Configure API keys for OpenAI, Hugging Face, and Google Gemini
• Explore LangChain architecture and core components
• Understand chains, agents, memory, document loaders, and prompt templates

Module 2: Building Q&A & Chat Apps

Estimated time: 1.5 hours

• Build a dynamic Q&A bot using OpenAI and Hugging Face LLMs
• Create a conversational chatbot with memory and context retention
• Deploy the chatbot on Hugging Face Spaces
• Test and refine interactions using real user queries

Module 3: Educational & Marketing Tools

Estimated time: 1.25 hours

• Develop a children’s object-learning educational app
• Design a marketing copy generator using prompt chaining
• Apply few-shot learning techniques for improved output
• Integrate structured prompt engineering for consistency

Module 4: ChatGPT Clone & Summarizer

Estimated time: 1 hour

• Build a ChatGPT-like interface with custom enhancements
• Add summarization layers to improve dialogue clarity
• Implement model chaining for multi-step reasoning

Module 5: Quiz App & CSV Analyzer

Estimated time: 1.5 hours

• Develop an MCQ quiz creator powered by LLMs
• Build a CSV data analyzer with natural language queries
• Integrate Pinecone for semantic search and retrieval

Module 6: Additional Use-case Apps

Estimated time: 1.5 hours

• Build a customer service call summarizer
• Create a content filter for moderating text
• Develop data extractors for structured output
• Extend functionality across projects 10–16

Module 7: Deployment & Front-end Integration

Estimated time: 1 hour

• Deploy AI apps using Streamlit with session state
• Host applications on Hugging Face Spaces
• Implement UI best practices and interactive widgets

Prerequisites

• Familiarity with Python programming
• Basic understanding of APIs and web services
• Access to API keys for OpenAI, Hugging Face, or Google Gemini

What You'll Be Able to Do After

• Build and deploy end-to-end AI applications using LangChain
• Integrate multiple LLMs including OpenAI, Hugging Face, LLaMA 2, and Gemini
• Implement RAG workflows with vector databases like Pinecone and FAISS
• Create interactive front-ends using Streamlit and Hugging Face Spaces
• Design practical AI tools for education, marketing, data analysis, and customer service