Complete AI Engineering Bootcamp: Agentic AI, RAG, LangGraph
Last updated 9/2026
Created by Pratham Uday Chandratre
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch Level: All Levels | Genre: eLearning | Language: English + subtitle | Duration: 216 Lectures ( 37h 12m ) | Size: 26.7 GB
Build production-ready AI applications with LLMs, LangChain, LangGraph, RAG and Agentic AI using end-to-end projects
What you'll learn
Master AI Engineering from LLMs and Prompt Engineering to Agentic AI, Multi-Agent Systems, and Enterprise AI. Build production-ready AI applications with Python, LangChain, and modern AI engineering best practices instead of simple demo projects. Design, build, and optimize Retrieval-Augmented Generation (RAG) systems using embeddings, vector databases, retrievers, and real-world document pipelines. Understand when to use Traditional RAG vs. Vectorless RAG, and make architecture decisions based on real engineering trade-offs rather than trends. Develop robust AI applications with Pydantic by implementing data validation, structured outputs, serialization, and production-grade data models. Create intelligent AI Agents with LangGraph that can reason, use tools, make decisions, manage memory, and execute multi-step workflows. Build Agentic RAG systems that intelligently retrieve, evaluate, rewrite, and generate grounded responses using modern AI workflows. Design and implement Multi-Agent AI Systems where multiple specialized AI agents collaborate to solve complex real-world problems. Build end-to-end AI projects including AI assistants, website summarizers, PDF chatbots, AI research agents, Agentic RAG systems, and Multi-Agent applications. Learn how modern AI systems are designed, debugged, optimized, and deployed using engineering principles followed in production environments. Compare leading AI models such as GPT, Claude, Gemini, and Llama, and choose the right model for different real-world AI applications. Think like an AI Engineer by understanding the principles behind modern AI architectures instead of simply memorizing frameworks and libraries. Gain practical experience through hands-on coding, mini-projects, and complete end-to-end capstone projects that reinforce every major concept. Build the confidence to design, develop, and extend modern AI systems using new frameworks and technologies as the AI ecosystem continues to evolve.Requirements
Basic Python programming knowledge (variables, loops, functions, and classes) is recommended. No prior AI, Machine Learning, LangChain, LangGraph, or RAG experience is required. A computer (Windows, macOS, or Linux) with an internet connection. A willingness to learn, build real-world AI projects, and experiment throughout the course.Description
Welcome to the Complete AI Engineering Bootcamp.
If you've ever felt overwhelmed by the rapidly changing world of Artificial Intelligence...
you're not alone.
Every week, there's a new AI model.
A new framework.
A new library.
A new "must-learn" technology.
One day it's Prompt Engineering.
The next it's LangChain.
Then RAG.
Then LangGraph.
Then Agentic AI.
And before you've mastered one technology...
another one appears.
The result?
Many aspiring AI Engineers spend months jumping from tutorial to tutorial, watching disconnected videos, and building small demo projects-yet still struggle to answer one simple question
"How do I actually become an AI Engineer capable of building real-world AI systems?"
That's exactly why this bootcamp exists.
This isn't just another course on LangChain...
or another course on RAG...
or another course on AI Agents.
This is a complete AI Engineering Bootcamp designed to take you from understanding the fundamentals of modern AI to building production-ready AI applications using today's most important technologies.
Throughout this journey, you'll learn how Large Language Models (LLMs), Prompt Engineering, LangChain, LangGraph, Retrieval-Augmented Generation (RAG), Vectorless RAG, Pydantic, Agentic AI, Multi-Agent Systems, and Enterprise AI all fit together into one complete engineering roadmap-not as isolated topics, but as connected building blocks of modern AI systems.
Whether your goal is to become an AI Engineer, build intelligent AI applications, or understand how modern production AI systems are actually designed, this bootcamp has been structured to help you progress one concept, one project, and one system at a time.
Section 2 - Why This Bootcamp Is Different
The world doesn't need another AI course that teaches disconnected frameworks or isolated tutorials.
It needs AI Engineers who understand how modern AI systems are actually designed, built, optimized, and deployed.
That's exactly the philosophy behind this bootcamp.
Rather than treating LangChain, LangGraph, Retrieval-Augmented Generation (RAG), Vectorless RAG, Prompt Engineering, Pydantic, and Agentic AI as separate topics, you'll learn how they work together as part of one complete AI Engineering ecosystem.
Every section has been carefully designed to build upon the previous one.
You'll begin by developing a deep understanding of Large Language Models (LLMs), Prompt Engineering, and the core concepts behind modern AI.
Then you'll apply those foundations to build real AI applications with Python and LangChain.
From there, you'll progressively move into Retrieval-Augmented Generation (RAG), Vectorless RAG, production AI engineering with Pydantic, intelligent AI Agents with LangGraph, Agentic RAG, Multi-Agent Systems, Human-in-the-Loop workflows, and complete enterprise-style AI applications.
Nothing is included simply because it's popular.
Every concept, every framework, and every project has been intentionally placed within the roadmap so that each new skill builds naturally on the one before it.
This means you're not just learning how to use modern AI frameworks.
You're understanding why they exist...
when to use them...
and how to combine them to design intelligent, production-ready AI systems.
Throughout the bootcamp, you'll write code alongside the lectures, build complete applications from scratch, debug real-world scenarios, and gradually develop the mindset of an AI Engineer rather than simply learning another programming library.
By the end of the journey, you'll have far more than a collection of tutorials.
You'll have a structured understanding of modern AI Engineering and a portfolio of practical projects that demonstrate how today's AI systems are designed and built. The curriculum includes end-to-end applications such as AI web apps, RAG systems, AI PDF chatbots, AI research agents, Agentic RAG workflows, and Multi-Agent AI systems.
Section 3 - What You'll Build Throughout This Bootcamp
This isn't a course where you'll simply watch lectures and copy a few lines of code.
This is a project-first AI Engineering Bootcamp.
Every major concept is reinforced by building real applications, giving you practical experience with the same technologies used in modern AI development.
Throughout this bootcamp, you'll progressively build a portfolio of complete AI systems, including
1. Intelligent AI applications powered by Large Language Models (LLMs)
2. Production-ready LangChain applications that combine prompts, chains, tools, and structured outputs
3.Retrieval-Augmented Generation (RAG) systems that answer questions using your own PDFs, documents, and knowledge bases
4. AI-powered PDF Chatbots capable of intelligent document search and conversational question answering
5. Vectorless RAG applications that demonstrate the next generation of AI retrieval without traditional vector databases
6. AI Agents built with LangGraph that reason, make decisions, use tools, and execute multi-step workflows
7. Intelligent AI Research Agents that search the web, analyze information, generate insights, and produce professional reports
8. Agentic RAG systems that intelligently retrieve, evaluate, rewrite, and generate grounded responses
9. Multi-Agent AI Systems where multiple specialized AI agents collaborate to solve complex tasks
10. Enterprise-style AI workflows featuring Human-in-the-Loop approvals, intelligent routing, evaluation, and production-ready architectures
And these aren't isolated coding exercises.
Every project is designed to introduce new engineering concepts while reinforcing everything you've learned in previous sections.
As the projects become more advanced, you'll naturally combine multiple AI technologies into complete systems-the same way modern AI applications are designed in real-world environments.
By the end of this bootcamp, you'll have built far more than individual examples.
You'll have a portfolio of practical AI applications that demonstrate your understanding of modern AI Engineering-from foundational LLM applications to production-ready Agentic AI systems. These projects align with the curriculum's progression through AI applications, RAG, Vectorless RAG, AI research agents, Agentic RAG, Multi-Agent Systems, and enterprise-style workflows.
Section 4 - A Structured Roadmap to Becoming an AI Engineer
One of the biggest challenges in learning Artificial Intelligence isn't the lack of information...
It's the lack of structure.
Thousands of tutorials, blog posts, YouTube videos, and documentation are available online, but they're often disconnected, outdated, or focused on solving a single problem without explaining the bigger picture.
This bootcamp was designed to solve that problem.
Instead of learning random AI frameworks in isolation, you'll follow a carefully structured roadmap that mirrors how modern AI Engineers develop their skills-from understanding the fundamentals of Large Language Models (LLMs) to designing sophisticated Agentic AI and Multi-Agent Systems for real-world applications.
You'll begin by mastering the core principles behind Generative AI, LLMs, Prompt Engineering, and how modern AI applications actually work.
Next, you'll learn how to build AI applications using LangChain, process documents, create embeddings, work with vector databases, and develop Retrieval-Augmented Generation (RAG) systems that can answer questions using your own data.
As your knowledge grows, you'll explore modern AI engineering techniques including Vectorless RAG, Pydantic for production-ready data validation, LangGraph for orchestrating intelligent AI workflows, Agentic RAG for advanced retrieval pipelines, and Multi-Agent Systems where specialized AI agents collaborate to solve complex tasks.
Along the way, you'll also learn the practical engineering skills that many courses overlook-working with APIs, environment configuration, structured outputs, debugging, application architecture, Streamlit interfaces, evaluation strategies, and production-oriented design patterns that make AI applications reliable and maintainable.
Every new concept builds naturally on previous lessons.
Every project reinforces multiple concepts.
Every section moves you one step closer to thinking and building like an AI Engineer.
By the end of this roadmap, you'll understand not only how to build modern AI applications, but also why different architectures, frameworks, and engineering decisions are used in real production environments.
Section 5 - Learn Complex AI Concepts the Right Way
Artificial Intelligence is one of the fastest-moving fields in technology.
But learning AI shouldn't feel confusing.
One of the biggest mistakes many learners make is jumping directly into advanced frameworks without first understanding the engineering principles behind them.
That's why this bootcamp follows a carefully designed teaching philosophy.
Rather than asking you to memorize code or copy projects, every major concept is explained from first principles before it's implemented in Python.
You'll understand why a technology exists...
what problem it solves...
and when it should be used in real-world AI applications.
Whether you're learning Prompt Engineering, LangChain, Retrieval-Augmented Generation (RAG), Vector Databases, Vectorless RAG, Pydantic, LangGraph, Agentic RAG, or Multi-Agent Systems, every topic is introduced step by step with clear visual explanations, practical coding sessions, and hands-on implementation.
As the course progresses, you'll gradually connect these individual concepts into complete AI systems, allowing you to understand not just individual frameworks, but the architecture of modern AI applications as a whole.
Instead of simply following tutorials, you'll learn to think like an engineer-analyzing problems, selecting the right tools, designing intelligent workflows, debugging AI applications, and making informed architectural decisions based on real-world requirements.
By the end of the bootcamp, you'll have more than theoretical knowledge.
You'll understand how modern AI applications are designed from the ground up and have the confidence to build, extend, and customize your own AI systems long after you've completed the course.
Section 6 - Build Skills That Go Beyond Tutorials
Artificial Intelligence is evolving rapidly, but one thing remains constant
Companies don't build AI products by combining random tutorials.
They build complete AI systems that retrieve information, reason through problems, interact with external tools, validate structured data, orchestrate intelligent workflows, and enable multiple AI agents to collaborate effectively.
That's exactly the type of engineering mindset this bootcamp is designed to develop.
Throughout this journey, you won't just learn individual frameworks-you'll understand how modern AI applications are architected from end to end.
You'll explore how Large Language Models (LLMs), Prompt Engineering, LangChain, Retrieval-Augmented Generation (RAG), Vector Databases, Vectorless RAG, Pydantic, LangGraph, Agentic AI, Multi-Agent Systems, and Human-in-the-Loop workflows fit together to create intelligent, reliable, and scalable AI applications.
Rather than stopping at simple demonstrations, you'll work through progressively more advanced applications that introduce concepts such as intelligent retrieval, reasoning-based workflows, AI tool integration, structured outputs, conversational memory, workflow orchestration, conditional routing, enterprise document processing, and collaborative AI agents.
By completing this bootcamp, you'll be equipped to understand the architecture behind many of today's AI-powered applications and have a strong foundation for designing your own solutions-whether you're building internal productivity tools, document intelligence systems, AI assistants, research workflows, or other intelligent applications.
Most importantly, you'll leave with a connected understanding of modern AI Engineering.
Instead of viewing LangChain, LangGraph, RAG, Agentic AI, and Multi-Agent Systems as separate technologies, you'll understand how they complement one another within complete AI solutions and how to make thoughtful engineering decisions when developing new applications.
Section 7 - Who Should Enroll in This AI Engineering Bootcamp?
Whether you're taking your first step into Artificial Intelligence or you're already working with AI tools and want to build more advanced applications, this bootcamp has been designed to help you grow with a structured, project-driven learning experience.
This bootcamp is an excellent fit if you are
An aspiring AI EngineerYou want to build a strong foundation in modern AI Engineering and learn how today's production AI systems are designed, developed, and connected-from Large Language Models (LLMs) and Prompt Engineering to LangChain, LangGraph, Retrieval-Augmented Generation (RAG), Agentic AI, and Multi-Agent Systems.
A Python Developer or Software EngineerYou already know Python and want to move beyond traditional software development by learning how to build intelligent AI applications, AI agents, document intelligence systems, and production-ready AI workflows.
A Data Scientist or Machine Learning EngineerYou understand machine learning fundamentals and want to expand your skill set into the rapidly growing field of Generative AI, LLM applications, AI orchestration, retrieval systems, and modern AI Engineering.
A Student Preparing for an AI CareerYou want one structured roadmap instead of spending months jumping between YouTube tutorials, documentation, blog posts, and disconnected online resources.
This bootcamp is designed to give you a logical learning path where every concept builds upon the previous one, helping you develop practical skills through real-world projects.
A Professional Transitioning into AIWhether you're coming from web development, backend development, automation, data analytics, DevOps, cloud engineering, or another technical background, this bootcamp will help you understand how modern AI applications are built using today's leading AI technologies.
A Builder Who Learns by CreatingIf you prefer building complete applications instead of only watching theory, you'll enjoy the hands-on approach used throughout this bootcamp.
From AI applications and Retrieval-Augmented Generation (RAG) systems to AI Research Agents, Agentic RAG workflows, and Multi-Agent AI systems, every major milestone is reinforced through practical implementation and end-to-end projects.
If you're looking for a bootcamp that simply introduces AI buzzwords, this may not be the right fit.
But if your goal is to understand modern AI Engineering, build intelligent AI applications from the ground up, and develop the confidence to design complete AI systems using technologies like LangChain, LangGraph, RAG, Agentic AI, and Multi-Agent Systems, then this bootcamp was created with that journey in mind.
Section 8 - Your AI Engineering Journey Starts Here
Artificial Intelligence is no longer a niche technology.
It is transforming how software is built, how businesses operate, and how people interact with technology every day.
As AI continues to evolve, understanding how to design intelligent, reliable, and production-ready AI systems is becoming an increasingly valuable engineering skill.
This bootcamp was created to provide a structured path through that journey.
From understanding the foundations of Large Language Models (LLMs) and Prompt Engineering...
to building intelligent AI applications with LangChain...
to developing Retrieval-Augmented Generation (RAG) systems...
to mastering Vectorless RAG, Pydantic, LangGraph, Agentic AI, Multi-Agent Systems, Human-in-the-Loop workflows, and complete enterprise-style AI applications...
every section has been designed to help you build practical skills through a logical progression of concepts and hands-on projects.
Whether your goal is to deepen your understanding of modern AI Engineering, create intelligent AI applications, build an impressive portfolio of AI projects, or gain confidence working with today's AI technologies, this bootcamp is designed to help you take meaningful steps toward those goals.
Learning AI is not about memorizing frameworks.
It's about understanding how different technologies work together to solve real problems.
It's about developing the confidence to design intelligent systems rather than simply following tutorials.
And it's about building the practical skills to continue learning as the field evolves.
If you're ready to move beyond isolated AI tutorials...
If you're ready to understand the engineering principles behind modern AI systems...
If you're ready to build complete AI applications with confidence...
Then I'd be delighted to have you join this bootcamp.
Let's begin your AI Engineering journey together.
Who this course is for
Aspiring AI Engineers who want a structured roadmap from AI fundamentals to production-ready Agentic AI systems. Python developers who want to build real-world AI applications using LLMs, LangChain, LangGraph, and modern AI frameworks. Software Engineers looking to transition into AI Engineering with practical projects instead of theory-heavy lectures. Data Scientists and Machine Learning Engineers who want to expand into Generative AI, RAG, AI Agents, and Multi-Agent Systems. Students and recent graduates preparing for careers in AI Engineering, Generative AI, or Intelligent Software Development. Working professionals who want hands-on experience building production-ready AI applications for real-world use cases. Developers who understand Python and want to learn how modern AI systems are actually designed, built, and optimized. Anyone who wants to master Retrieval-Augmented Generation (RAG), Agentic RAG, and Vectorless RAG through practical implementation. Learners who prefer building real projects instead of only watching theory or following isolated coding tutorials. Professionals who want to stay current with modern AI Engineering, including AI Agents, Multi-Agent Systems, and Enterprise AI workflows.
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