LangChain Course: Build Production AI with RAG & Agents

About this course

Master LangChain by building 4 production AI systems from scratch. This hands-on course skips theory and teaches you to build real AI applications that handle actual business problems.

What You'll Build:

Email Classifier - Route customer emails automatically using Pydantic schemas for type-safe validation. Learn temperature settings, confidence scoring, and fallback patterns that prevent production crashes. Achieve 70% accuracy on real datasets.

RAG QA Bot - Create a documentation question-answering system using retrieval-augmented generation. Master vector embeddings with FAISS, semantic search, document chunking strategies, and hallucination prevention. Build bots that search your docs instead of inventing answers.

Meeting Task Extractor - Parse messy meeting notes into structured action items with owners, deadlines, and commitment levels. Master nested Pydantic models, optional fields, and advanced prompt engineering that handles pronouns and ambiguous phrasing.

Data Analysis Agent - Build autonomous AI agents that write and execute their own pandas code to answer business questions. No SQL knowledge required—ask "Which product made the most revenue?

" and get instant answers.

LangChain Fundamentals - Master the 4 core abstractions (Prompts, Models, Parsers, Chains) that power every AI application. Understand chains vs agents, when to use each, and production deployment patterns.

Production-Ready Patterns - Handle errors gracefully, implement retry logic, optimize token costs with GPT-4o-mini, prevent hallucinations with grounded prompts, and debug agent thinking processes.

Everything uses real data: actual CSVs, meeting transcripts, documentation files. No toy examples. You'll write production-quality code with proper error handling, validation, and cost optimization.

Perfect for Python developers, backend engineers, data analysts, and a

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