Move beyond basic chatbot tutorials and learn how to build complete, practical, and production-ready AI applications in just 14 days.

AI Engineering Bootcamp: Apps, RAG, Agents & MCP is a hands-on, project-based course designed to take you from the foundations of Generative AI to advanced AI agents, Retrieval-Augmented Generation, Model Context Protocol, multi-agent systems, and production AI engineering.

You will begin by understanding how Large Language Models, prompts, tokens, responses, and AI application architectures work. You will build your first terminal and Streamlit AI chatbot, create reusable LLM service layers, and learn how to connect Python applications to cloud-based or local AI models such as OpenAI and Ollama.

The course then introduces practical prompt engineering techniques. You will learn how to structure prompts using roles, tasks, context, rules, examples, and output formats. Through a hands-on prompt playground, you will experiment with reusable prompt templates and understand how better prompts lead to more reliable AI applications.

Next, you will build real-world projects such as an AI Resume Analyzer, a PDF Chat Assistant, and an Autonomous Research Agent. You will learn how to process documents, extract text, create embeddings, split content into chunks, store vectors, and perform semantic search using tools such as ChromaDB.

You will explore both beginner and advanced RAG systems. Topics include vector databases, hybrid retrieval, reranking, metadata filtering, query transformation, Knowledge Graph RAG, and Agentic RAG. You will use these concepts to build an enterprise-ready AI Knowledge Assistant capable of answering questions from business documents and private data.

The course also provides a deep introduction to AI agent engineering. You will learn how agents combine LLMs, tools, memory, planning, workflows, and state to complete complex tasks. You will build autonomous agents that can research topics, generate reports, use external tools, interact with websites, and evaluate their own outputs.

You will also build multi-agent AI systems using orchestration patterns such as planner, researcher, analyst, writer, and reviewer. You will explore LangGraph, agent communication, shared state, task delegation, and enterprise multi-agent architectures.

A major part of the course focuses on the Model Context Protocol, commonly known as MCP. You will learn how MCP allows AI applications to connect securely with tools, files, APIs, databases, and enterprise systems. You will build your own MCP server and understand how to design multi-server MCP ecosystems.

Additional topics include browser agents, multimodal AI, vision models, voice AI, evaluation, monitoring, observability, guardrails, responsible AI, governance, deployment, and production reliability.

By the end of this course, you will have built a strong portfolio of AI engineering projects, including chatbots, RAG applications, autonomous agents, browser automation systems, MCP tools, multi-agent workflows, and a complete production AI platform.

This course is ideal for Python developers, AI enthusiasts, software engineers, data professionals, students, and anyone who wants to become an AI Engineer, Generative AI Developer, RAG Developer, or Agentic AI Engineer through practical, hands-on learning.