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LangChain & LangGraph Mastery RAG, Agents & AI Workflows

Name: LangChain &amp; LangGraph Mastery RAG, Agents &amp; AI Workflows
Author: Baturi

Beginner to Advanced, Prompting, Structured Output, RAG, Vector Stores, Retrievers, Agents & LangGraph Workflows with Py
What you'll learn
✓ Understand the complete LangChain architecture and how LLM-powered applications are designed end to end.
✓ Integrate multiple LLM providers such as OpenAI, Hugging Face, Gemini, and Anthropic into LangChain applications.
✓ Design reusable and maintainable prompts using PromptTemplate and ChatPromptTemplate.
✓ Generate reliable, machine-readable AI outputs using structured outputs and output parsers.
✓ Load, preprocess, and manage documents for real-world AI use cases using document loaders.
✓ Apply advanced text splitting strategies to optimize context, retrieval, and embedding quality.
✓ Create and manage embeddings for semantic search and Retrieval-Augmented Generation (RAG).
✓ Implement vector stores and perform similarity search using FAISS, Chroma, and other backends.
✓ Build and fine-tune retrievers for accurate and scalable RAG pipelines.
✓ Design and orchestrate complex AI workflows and tool-calling agents using LangGraph.
Requirements
● Basic knowledge of Python programming is required.
● Familiarity with fundamental programming concepts such as functions, variables, and classes.
● Basic understanding of APIs is helpful but not mandatory.
● A system with Python installed and internet access for LLM APIs.
Description
This comprehensive course is designed to take you from the fundamentals of LangChain to advanced, production-ready AI workflows using LangGraph. It is a complete, end-to-end guide for developers, data scientists, and AI engineers who want to build real-world applications powered by Large Language Models (LLMs).
Unlike surface-level tutorials, this course focuses on how modern LLM systems are actually built in practice — from prompt design and structured outputs to embeddings, vector databases, retrievers, full RAG pipelines, and finally workflow orchestration using LangGraph.
You will not only understand how each LangChain component works individually, but also how they connect together to form scalable, maintainable, and production-ready AI systems.
What Makes This Course Different
This course is built with a code-first, system-level approach. Every concept is explained in detail and then implemented step by step using real examples. By the end of the course, you will have a clear mental model of how LLM applications are architected in the real world.
You will learn how to
• Control LLM behavior using structured prompting and output parsing
• Build reliable pipelines instead of fragile prompt hacks
• Design Retrieval-Augmented Generation (RAG) systems that actually scale
• Move from linear chains to graph-based workflows using LangGraph
LangChain Fundamentals & LLM Integration
You begin with a strong foundation
• What LangChain is, why it exists, and how it fits into the modern Generative AI ecosystem
• Python setup and LangChain installation
• Integration with popular LLM providers such as OpenAI, Hugging Face, Anthropic, Gemini, and others
• Understanding core concepts like Chains, Agents, Memory, and Tools
This section ensures that even beginners can confidently follow the rest of the course.
Prompt Templates & Prompt Engineering in Practice
Prompting is treated as a software engineering problem, not trial and error.
You will learn
• PromptTemplate, ChatPromptTemplate, and MessagesPlaceholder
• Dynamic prompt creation using variables and formatting
• Best practices for reusable, maintainable, and safe prompt design
• Real-world prompt use cases for chatbots, assistants, and QA systems
Structured Outputs & Output Parsers
One of the most critical parts of production AI systems is reliable output.
In this section, you will master
• Structured outputs using JSON, Pydantic, and TypedDict
• LangChain's with_structured_output() helper
• Output parsers such as StrOutputParser, JSONOutputParser, and PydanticOutputParser
• Converting unpredictable LLM text into clean, machine-readable data
• Handling validation, type safety, and downstream integrations
This is essential for building AI systems that interact with databases, APIs, and business logic.
Document Loaders & Text Splitters
You will learn how real data enters an LLM system.
Topics include
• Document loaders for PDFs, CSVs, JSON, text files, and directories
• Understanding the Document object (content + metadata)
• Text splitters and why chunking strategy matters
• Character, Recursive, Markdown, and specialized splitters
• Best practices for chunk size, overlap, and semantic preservation
This section builds the foundation for high-quality retrieval and RAG pipelines.
Embeddings & Vector Stores
This course gives deep, practical coverage of embeddings and vector databases.
You will learn
• What embeddings are and how semantic similarity works
• LangChain's embedding interface and methods
• Providers such as OpenAI, Hugging Face, Cohere, Google, Mistral, and others
• Vector stores like FAISS, Chroma, In-Memory, and production options
• How to store, search, and retrieve embeddings efficiently
• Best practices for performance, cost, and scalability
Retrievers & Advanced Retrieval Strategies
Retrievers are the heart of Retrieval-Augmented Generation.
You will master
• Vector-based retrievers
• Sparse and BM25 retrievers
• Hybrid and ensemble retrievers
• Contextual compression and multi-query retrievers
• Tuning retriever parameters for accuracy and relevance
• Building custom retrievers for specialized use cases
Building a Complete RAG Application
This course includes a full end-to-end RAG project.
You will
• Load and preprocess documents
• Split and embed data
• Store embeddings in a vector database
• Configure retrievers
• Combine retrieved context with prompts
• Generate accurate, grounded responses
By the end, you will be able to design and implement your own production-ready RAG systems.
LangGraph: AI Workflow & Agent Orchestration
The final part of the course introduces LangGraph, LangChain's next-generation framework for complex AI workflows.
You will learn
• Core LangGraph concepts: nodes, edges, state, and execution flow
• How LangGraph differs from traditional chains
• Building simple and advanced workflows
• Tool-calling agents with branching logic
• Managing memory, state, and multi-step reasoning
• Best practices for debugging and scaling AI workflows
This section prepares you for building enterprise-grade, multi-agent AI systems.
Who This Course Is For
• Python developers who want to build real LLM applications
• Data scientists transitioning into Generative AI
• AI engineers working with RAG, embeddings, and agents
• Anyone who wants to move beyond basic prompt engineering
Basic Python knowledge is recommended. No prior LangChain or LangGraph experience is required.
By the End of This Course, You Will Be Able To
• Build complete LangChain-based AI applications from scratch
• Design reliable prompting and structured output pipelines
• Implement scalable RAG systems using vector databases
• Create advanced retrievers for real-world use cases
• Orchestrate intelligent AI workflows using LangGraph
• Confidently move from experimentation to production
If you want to seriously master LangChain and LangGraph, understand how modern AI systems are built, and gain skills that are directly applicable in real projects, this course is designed for you.
Who this course is for
■ Python developers who want to build real-world LLM and Generative AI applications.
■ Data scientists transitioning into Retrieval-Augmented Generation and AI engineering roles.
■ AI engineers looking to design scalable, production-ready RAG and agent-based systems.
■ Software engineers interested in LangChain, LangGraph, and modern AI workflows.
■ Beginners in Generative AI who want a structured, end-to-end learning path.
Homepage

https://www.udemy.com/course/langchain-langgraph-mastery-rag-agents-ai-workflows/

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LangChain & LangGraph Mastery RAG, Agents & AI Workflows

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