Langchain Course: Build Production Ai With Rag & Agents
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.39 GB | Duration: 2h 28m
Build 4 Real AI Projects: Email Classifier, RAG QA Bot, Task Extractor & Data Analysis Agent with Python & GPT-4
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
Requirements
Basic Python fundamentals: You should understand variables, functions, loops, dictionaries, and lists. We'll teach you the AI-specific Python as we go.
Ability to run Python scripts: Know how to use pip to install packages and run .py files from terminal/command prompt-we'll guide you through environment setup.
OpenAI API key: You'll need an API key from OpenAI (we show exactly how to get one). Course projects cost approximately $0.50-$2.00 total to run using GPT-4o-mini.
Text editor or IDE: VS Code recommended (free)-we use it throughout the course, but any Python editor works fine.
Enthusiasm for building real AI projects: We skip theory-heavy lectures and dive straight into building production systems you can deploy immediately.
No prior LangChain, AI, or machine learning experience needed-we start from absolute basics and build up to advanced autonomous agents step by step with detailed explanations.
Description
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 anyone building AI-powered applications. Total API cost to complete course: under $2 using GPT-4o-mini.By the end, you'll have 4 portfolio-ready projects and understand how to build, debug, and deploy LangChain systems that actually work in production.
Python developers who want to add AI capabilities to their applications without getting a PhD in machine learning-learn by building real projects.,Backend engineers and full-stack developers integrating LangChain into production systems for email automation, chatbots, document processing, or data analysis.,Data analysts and business intelligence professionals who want to automate repetitive pandas tasks and answer business questions with AI agents instead of manual SQL queries.,Product managers and technical founders building AI-powered SaaS products who need to understand LangChain architecture and what's actually possible in production.,Software engineers tired of theory-only AI courses who want hands-on projects with real code, actual CSVs, production error handling, and deployment patterns.,Automation enthusiasts looking to eliminate repetitive work: auto-routing emails, extracting meeting action items, answering documentation questions, or analyzing sales data.