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What You'll Learn

Build practical AI applications using Python
Streamlit
and Large Language Models.,Understand modern AI concepts including Generative AI
LLMs
tokens
prompts
context windows
and hallucinations.,Write effective prompts using roles
instructions
constraints
examples
and structured output formats.,Create a Prompt Engineering Playground to test
compare
and save reusable prompts.,Build an AI Resume Analyzer that reviews resumes
scores them
and suggests improvements.,Extract text from PDFs and documents for use in AI applications.,Build a PDF Chat Assistant using Retrieval-Augmented Generation
also known as RAG.,Understand embeddings
semantic search
document chunking
and vector databases.,Use ChromaDB as a local vector database for document search and retrieval.,Build an autonomous AI Research Agent that can plan
search
analyze
write
review
and save reports.,Create a multi-agent workflow with Planner
Researcher
Writer
Editor
and QA agents.,Package an AI application with Docker and prepare it for portfolio or deployment.,Apply responsible AI practices including privacy
accuracy
guardrails
and human oversight.,Create portfolio-ready AI projects suitable for GitHub
resumes
interviews
and demos.

Requirements

No advanced AI
machine learning
or data science background is required.,Basic Python knowledge is helpful
but the course is beginner-friendly and explains the code step by step.,Basic command line or terminal knowledge is helpful for running Python apps and installing packages.,Students should have a computer with internet access.,An OpenAI API key is optional. Students can also use Ollama to run local models where supported.,Basic familiarity with APIs
web apps
or software development is helpful
but not required.,No advanced math is required.,No prior experience with RAG
AI agents
vector databases
Streamlit
ChromaDB
or Docker is required. These topics are introduced from the ground up through hands-on labs.,Most importantly
students should be curious and ready to build practical AI projects step by step.

Description

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.

Who this course is for:

This course is for beginners and intermediate learners who want to build practical AI applications instead of only learning AI theory.,It is ideal for software developers who want to add AI
LLMs
RAG
and AI agents to their skill set.,It is also useful for students who want portfolio-ready AI projects for GitHub
resumes
internships
interviews
or job applications.,Analysts
managers
consultants
and business professionals who want to understand how AI applications are built will also benefit from this course.,Entrepreneurs and creators who want to prototype AI-powered products
productivity tools
research assistants
resume tools
document chatbots
or business assistants will find the projects practical and reusable.,This course is also a good fit for Python learners who want to move beyond basic scripts and start building real AI-powered applications with Streamlit
LLM APIs
RAG
ChromaDB
agents
and Docker.,This course is not designed for learners looking for deep machine learning theory
advanced mathematics
or model training from scratch. The focus is practical AI application development.