The Complete LangChain Bootcamp Build LLM‑Powered Apps 2026

Master LangChain from zero to production:15+ real projects, RAG, agents, multi‑agent systems LangGraph and deployment
What you'll learn

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Install and configure LangChain for local development and production.
Understand LLM fundamentals: tokens, context windows, and generation parameters
Work with OpenAI, HuggingFace, and local Ollama models.
Craft effective prompts using system/human/AI messages, few‑shot selectors, and output parsers
Build chains with LCEL, sequential flows, conditional branching, and parallel execution.
Implement Retrieval‑Augmented Generation (RAG) with loaders, splitters, embeddings, Chroma, and FAISS.
Manage conversational state with buffer, summary, entity, and custom memory.
Create autonomous agents that use built‑in and custom tools (search, calculator, APIs).
Master advanced retrieval: multi‑query, self‑query, parent document, ensemble, and compression
Evaluate and debug apps with LangSmith, custom evaluators, callbacks, and A/B testing.
Serve LangChain apps with FastAPI and Streamlit; deploy to Fly io, AWS, and Streamlit Cloud.
Secure LLM apps, manage API keys, prevent prompt injection, and monitor production.
Build multi‑agent systems with planning, reflection, and human‑in‑the‑loop workflows.
Work with LangGraph, LangServe, multimodal GPT‑4o, semantic caching, and Kubernetes scaling.
Requirements

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Basic Python knowledge (functions, classes, pip, virtual environments).
An OpenAI API key (free trial credits are enough to start).
A computer with Python 3.9+ and an internet connection.
No prior experience with LangChain or LLM frameworks is required.
Description

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Unlock the full power of Large Language Models and master LangChain, the most popular framework for building LLM‑powered applications.The Complete LangChain Bootcamp takes you from absolute beginner to production‑ready AI engineer with80 hands‑on lectures across12 comprehensive sections.
You'll start with LLM fundamentals-tokens, model APIs, OpenAI and open‑source models, and theLangChain Expression Language (LCEL). Then you'll dive deep intoprompt engineering: reusable templates, few‑shot selectors, structured output parsers, and prompt versioning. You'll build powerfulRetrieval‑Augmented Generation (RAG) systems with document loaders, text splitters, embeddings, Chroma, FAISS, and advanced retrievers like self‑query, parent‑document, and ensemble retrievers.
The course coversstateful conversations using buffer, summary, entity, and custom memory. You'll then masteragents and tools: ReAct, OpenAI function calling, custom tools, and personal assistant agents. Advanced agent patterns follow-self‑critique, planning, hierarchical agents, multi‑agent collaboration, and agentic RAG.
Production matters: you'll learnLangSmith tracing and evaluation, A/B testing, FastAPI serving, caching, security, cloud deployment, and monitoring. New sections covermultimodal chains with GPT‑4o,LangGraph workflows,LangServe one‑command APIs, semantic caching, Kubernetes scaling, and LLM security.
Every lecture is delivered as a Jupyter Notebook with ready‑to‑run code, clear explanations, pro tips, and exercises with solutions. You'll build15+ real projects, including a PDF chatbot, legal search engine, automated evaluation pipeline, multi‑agent research team, and a full‑stack multimodal SaaS app. By the end, you'll have the skills to design, deploy, and scale sophisticated LangChain applications in production. Enroll now and become an LLM engineer.
Who this course is for

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Python developers who want to integrate LLMs into real applications.
Data scientists and machine learning engineers exploring generative AI.
AI enthusiasts and tinkerers building chatbots, agents, and document‑QA systems.
Software engineers looking to add LLM‑powered features to existing products.
Entrepreneurs and indie hackers who want to ship AI SaaS apps fast.
Homepage

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