Master LangChain #2 | Why LangChain & How It Works with Architecture

#langchain #generativeai #aiframeworks #openai #azureopenai #aiapps #promptengineering #llm #aiintegration #rag #aiagents #machinelearning #artificialintelligence

Are you wondering *why developers use LangChain* instead of directly calling LLM APIs like OpenAI or Azure OpenAI? 🤔

In this tutorial, we’ll break down:

✅ *Why LangChain?* – The problems with raw LLM APIs (no memory, no workflows, no tool integration).
✅ *How LangChain Works* – The full architecture: **Models → Prompts → Memory → Chains → Agents → Tools**.
✅ *Examples & Code Walkthroughs* – From simple prompt templates to *Azure OpenAI + ChromaDB RAG* and agents.

By the end, you’ll understand not just **what LangChain is**, but also **why it’s essential for building production-ready AI apps**.

📘 Topics Covered:

Raw API vs LangChain
LangChain building blocks (Models, Prompts, Memory, Chains, Agents, Tools)
High-level architecture explained with diagrams
How to extend LangChain with memory, retrieval, and external tools

👉 Perfect for developers, AI enthusiasts, and anyone exploring **Generative AI application development**.

📺 Watch the full LangChain series here: \[   • LangChain Mastery: From Zero to LLM-Powere...  ]