What you’ll learn
- Build production-ready AI systems using LangChain with structured outputs, RAG pipelines, and autonomous agents that handle real business problems
- Master LangChain fundamentals: prompts, models, output parsers, and chains—the 4 core abstractions that power every AI application
- Create type-safe AI outputs with Pydantic schemas including nested models, optional fields, and automatic validation to prevent production crashes
- Design bulletproof prompts with format instructions, concrete examples, and edge case handling that eliminate hallucinations and malformed JSON
- Build retrieval-augmented generation (RAG) systems using FAISS vector stores, semantic search, and document chunking for accurate question-answering
- Implement vector embeddings and similarity search to find relevant information across thousands of documents in milliseconds
- Develop AI agents that write their own code using pandas to analyze data, answer business questions, and generate insights automatically
- Handle production failures gracefully with fallback patterns, error handling, retry logic, and confidence scoring for robust AI systems
- Extract structured data from unstructured text: parse meeting notes into action items, emails into categories, and documents into databases
- Configure ChatGPT-4o-mini and OpenAI API with proper temperature settings, token limits, and cost optimization strategies
- Deploy 4 real-world AI projects: Email Classifier (70% accuracy), RAG QA Bot (handles 5 question types), Meeting Task Extractor (nested data), Data Analysis Age
- Understand when to use (and avoid) LangChain, difference between chains vs agents, and production deployment best practices
