AI & Agentic AI Tutorials
Deep-dive tutorials on RAG systems, AI agents, LangChain, LangGraph, and production AI engineering. Written by engineers who build these systems daily.
Agentic AI Course India: Complete 2026 Guide to Building AI Agents
India's AI industry is projected to reach $17 billion by 2027, and the demand for engineers who can build autonomous AI agents has never been higher. This guide walks you through everything you need to know about choosing and succeeding in an agentic AI course in India, from curriculum essentials to career outcomes.
RAG Systems Tutorial: Build Retrieval-Augmented Generation from Scratch
Retrieval-Augmented Generation (RAG) is the most practical technique for making LLMs work with your own data. This tutorial takes you from zero to a fully functional RAG pipeline, covering embeddings, vector stores, chunking strategies, and the retrieval-generation loop that powers modern AI applications.
LangChain Tutorial 2026: Complete Guide for Python Developers
LangChain has evolved from a simple LLM wrapper into the most widely adopted orchestration framework for AI applications. This 2026 tutorial covers the modern LangChain stack including LCEL, tool calling, structured outputs, and production patterns that every Python developer needs to know.
LangGraph Tutorial for Beginners: Build Stateful AI Agent Workflows
LangGraph is the framework of choice for building AI agents that need to maintain state, make decisions, and execute multi-step workflows. This beginner-friendly tutorial teaches you the graph-based paradigm from first principles, with hands-on examples you can run immediately.
MCP Protocol Tutorial: Model Context Protocol Explained for AI Developers
The Model Context Protocol (MCP) is an open standard for connecting AI models to external tools and data sources. Created by Anthropic, MCP provides a universal interface that lets any AI application securely access any data source. This tutorial explains MCP architecture, implementation, and practical use cases.
CrewAI vs LangGraph 2026: Which AI Agent Framework Should You Use?
Choosing between CrewAI and LangGraph is one of the most common decisions AI engineers face in 2026. Both frameworks enable multi-agent systems, but they take fundamentally different approaches. This comparison gives you the technical depth to make the right choice for your project.
OpenAI Agents SDK Tutorial: Build Production AI Agents
The OpenAI Agents SDK provides a streamlined way to build production-grade AI agents using OpenAI models. This tutorial covers the SDK's core primitives, including agent loops, handoffs between specialized agents, guardrails for safety, and tracing for observability.
Production RAG Systems: From Prototype to Enterprise-Grade Retrieval
The gap between a RAG prototype and a production system is vast. This tutorial bridges that gap with battle-tested strategies for scaling retrieval, improving answer quality, reducing hallucinations, and monitoring system health in enterprise environments.
Multi-Agent AI Systems: Build Teams of Collaborating AI Agents
Single agents hit a ceiling. Multi-agent systems break through it by combining specialized agents that collaborate, delegate, and self-correct. This tutorial teaches you the architecture patterns, communication protocols, and implementation techniques for building reliable multi-agent systems.
GenAI Engineering Masterclass: From LLMs to Production AI Systems
Generative AI engineering is the discipline of building reliable, scalable AI systems on top of large language models. This masterclass covers the full stack, from understanding how LLMs work internally to deploying and monitoring production systems that serve thousands of users.
Build AI Agents with Python: Step-by-Step Developer Guide 2026
This hands-on guide takes you from writing your first AI agent to building production-ready autonomous systems in Python. Every concept is illustrated with working code that you can run, modify, and extend for your own projects.
Agentic RAG Tutorial: Self-Correcting Retrieval with LangGraph
Standard RAG pipelines are fragile: if retrieval fails, the entire response fails. Agentic RAG adds an intelligence layer that evaluates retrieval quality, retries with reformulated queries, routes to alternative sources, and self-corrects before responding. This tutorial shows you how to build it with LangGraph.
Best AI Course with Projects India 2026: Hands-On GenAI & Agentic AI
The best AI courses in India are defined not by lectures but by projects. In 2026, hiring managers look for demonstrated ability to build and deploy AI systems. This guide evaluates the top project-based AI courses available to Indian learners and helps you choose the one that aligns with your career goals.
Deploy AI Agents to Production: Docker, FastAPI & Monitoring Guide
Building an AI agent is half the battle. Deploying it reliably to serve real users is the other half. This guide covers the complete production deployment pipeline: containerization with Docker, API design with FastAPI, observability with LangSmith, and operational best practices for running AI systems at scale.
AI Agent Interview Questions 2026: Top 50 Questions & Answers
AI agent engineering interviews test a unique combination of LLM knowledge, system design skills, and production engineering experience. This guide covers the 50 most frequently asked questions across five categories, with detailed answers that reflect what top companies are actually looking for in 2026.