WHO THIS IS FORPython developers and practical AI builders
01LLM APPLICATION ENGINEERING
LangChain for GenAI and AI Agents Handbook
Build practical LLM, RAG and agentic AI applications with Python—from model calls and structured responses to tools, memory, observability and Docker deployment.
Connected learning pathBeginner-friendlyReal project scenarios
Start with Byte 01 Python→Model→Knowledge→Tools
HANDBOOK OVERVIEW
See the whole system.
Then master each decision.
This is a connected learning path—not a collection of isolated tips. Every Byte builds the mental model, makes the workflow visible and applies it to a situation you could meet in a real project.
Designed for practical understandingFinish ready to recognise, apply and explain the concept.
STARTING POINTBasic Python; no LangChain experience required
02WHAT YOU WILL BUILDA model → RAG → agent → production learning path
03THE MAANAVAN LEARNING METHODOne clear progression in every Byte
Move from first understanding to confident explanation.
UNDERSTANDBuild the mental modelStart with plain language and a familiar analogy.VISUALISEFollow the workflowSee how each component connects and why it matters.APPLYEnter a real scenarioUse the concept in a practical project situation.EXPLAINMake the decision clearFinish with mistakes, takeaways and an interview answer.
CONNECTED LEARNING PATH
Learn in the order the work happens.
Complete one focused concept at a time. Every chapter includes practical guidance you can use immediately.
START5 guided BytesPRACTICAL OUTCOME
BYTE 01 OF 05 · Generative AI & AgentsUnderstand LangChain
beginner · 28 minWhat Is LangChain? Build LLM Applications
Understand models, messages, prompts and structured outputs—and where LangChain fits when a simple model call grows into a practical AI application.
AFTER THIS BYTEBuild a clear foundation
BYTE 02 OF 05 · Generative AI & AgentsBuild chains
beginner · 32 minBuild Reusable Prompts and Chains with LangChain
Turn one model call into a reusable LangChain prompt-to-model-to-output pipeline, then validate structured results before Python uses them.
AFTER THIS BYTEFollow the working process
BYTE 03 OF 05 · Generative AI & AgentsBuild RAG
beginner · 34 minBuild RAG Applications with Your Own Documents
Build the mental model for document loading, chunking, embeddings, vector search and grounded answers through a practical policy assistant.
AFTER THIS BYTEConnect the concept to practice
BYTE 04 OF 05 · Generative AI & AgentsAdd memory
beginner · 32 minAdd Conversation Memory to LangChain Applications
Understand thread-level memory, preserve follow-up context safely and prevent different users’ conversations from mixing.
AFTER THIS BYTERecognise risks and controls
BYTE 05 OF 05 · Generative AI & AgentsBuild safe agents
beginner · 36 minBuild LangChain Agents with Tools and Safety Controls
Give a LangChain agent bounded Python tools, observe its decisions and require explicit human approval before consequential external actions.
AFTER THIS BYTEApply it with confidence