LangChain
LangChain Fundamentals
Last Updated: July 25, 2026In the rapidly evolving world of Artificial Intelligence, building applications that leverage Large Language Models (LLMs) has become the new...
Components of LangChain
Last Updated: July 25, 2026Building production-ready applications powered by Large Language Models (LLMs) requires more than just sending a prompt to an API. It...
Deep Dive into LangChain Models
Last Updated: July 25, 2026In the world of Generative AI development, the Model component in LangChain serves as the crucial foundation for interacting with...
Prompts in LangChain
Last Updated: July 25, 2026In the world of Generative AI, Prompts are the messages we send to an LLM to get a response. While...
Structured Output in LangChain
Last Updated: July 25, 2026In the evolving landscape of Generative AI, the focus is shifting from how humans interact with Large Language Models (LLMs)...
Output Parsers in LangChain
Last Updated: July 25, 2026In the world of Generative AI, Large Language Models (LLMs) typically communicate in plain, unstructured text. While this is fine...
Chains in LangChain
Last Updated: July 25, 2026In LangChain, a Chain is a way to connect multiple individual components into a single pipeline. Instead of manually handling...
LangChain Runnables: The DNA of AI Pipelines
Last Updated: July 25, 2026In the early days of LangChain, building LLM applications was a “manual” process. Developers had to juggle different methods for...
Document Loaders in LangChain
Last Updated: July 25, 2026Building on our previous discussions about Chains and Runnables, the next step in creating advanced LLM applications—specifically RAG (Retrieval-Augmented Generation)—is...
Text Splitters in LangChain
Last Updated: July 25, 2026In the journey of building RAG (Retrieval-Augmented Generation) applications, after loading your documents, the next critical step is Text Splitters....
Vector Stores in LangChain: The Memory of Your AI
Last Updated: July 25, 2026In traditional applications, we use relational databases (like MySQL or Oracle) to store data. However, these systems are built for...
Retrievers in LangChain: The Search Engine for Your AI
Last Updated: July 25, 2026A Retriever is a component in LangChain that fetches relevant documents from a data source in response to a user’s...
Retrieval-Augmented Generation
Last Updated: July 25, 2026In the world of Generative AI, Retrieval-Augmented Generation (RAG) is arguably the most common and useful application. While Large Language...
YouTube RAG Chatbot Project
Last Updated: July 25, 2026YouTube RAG Chatbot is an AI-powered web application that lets you have a conversation with any YouTube video. Instead of watching...
Tools in LangChain
Last Updated: July 25, 2026In a standard AI system, an LLM possesses two core capabilities: Reasoning (the ability to think and break down a...
AI Agents: The Autonomous Future of LangChain
Last Updated: July 25, 2026An AI Agent is an intelligent system that receives a high-level goal from a user and autonomously plans, decides, and...
Top 100 LangChain Interview Questions & Expert Guide for GenAI Developers
Last Updated: July 29, 2026Section 1: LangChain Fundamentals (Basic) Section 2: Models & Configuration (Basic/Intermediate) Section 3: RAG & Indexing (Intermediate) Section 4: Chains...