AI Hero: 7-Day AI Agents Crash-Course

Build a complete AI agent from data ingestion to deployment in 7 focused days.

You will use a real GitHub repository as the source material, then build an assistant that can read the project, search through its docs and code, answer questions with citations, and ship as a small web app.

This is not a toy chatbot course. By the end, you will have a portfolio-ready AI project with code, evaluation, a demo, and a clear README.

What your agent will be able to do

Your agent will understand a real codebase, not a tiny example file. You can use your own repository, or choose an open-source project if you do not have one ready.

During the course, you will build an assistant that can:

• Read documentation, code, issues, and comments from a GitHub repository
• Split large files into useful chunks for retrieval
• Search with lexical search, semantic search, and hybrid search
• Answer project-specific questions using RAG
• Use tools and function calling for more useful responses
• Keep answers grounded with citations and logs
• Evaluate the quality of search and agent responses
• Run behind a simple Streamlit interface
• Be shared as a portfolio project

Example question:

How does authentication work in this project?

By the end of the course, your agent should be able to answer questions like this by looking at the project files it indexed and pointing you to the relevant parts of the codebase.

What you need

Plan for 1-2 hours per day. The lessons are practical and build on each other, so it is better to do a little every day than to read everything at once.

You will need:

• Basic Python skills: functions, classes, virtual environments, and pip
• Comfort with the command line
• A GitHub repository to analyze, or an open-source repository you want to study
• An OpenAI API key for the hands-on exercises
• A laptop where you can run Python locally

The OpenAI API cost for the course is usually small. For most projects, expect a few euros or dollars, depending on the size of the repository and how much you experiment.

What you will build in 7 days

Each day adds one piece to the same project.

• Day 1: ingest and index your data from GitHub
• Day 2: chunk and prepare the data for search
• Day 3: add lexical, semantic, and hybrid search
• Day 4: turn the search system into an agent with tools
• Day 5: add logging, citations, and evaluations
• Day 6: publish the agent with a Streamlit UI
• Day 7: write the README, record a demo, and share the result
• Certificate: submit the finished project and get the completion certificate

The syllabus below contains the detailed lessons and homework for each day.

What you will have at the end

When you finish, you will have a working AI assistant that understands a GitHub project and can answer questions about it. You will also have the supporting pieces that make it useful outside a notebook:

• Source code you can keep extending
• A documented ingestion and search pipeline
• An agent with tools and prompts
• Evaluation data and checks
• A deployed web interface
• A README with screenshots or a demo video
• A certificate of completion after project submission

The goal is to leave with something you can show: in a portfolio, in a job conversation, or as a base for your next AI engineering project.

Frequently asked questions

What makes this different from other AI tutorials?

You build with a real GitHub project, not a generic demo. The course connects the full path from data ingestion to search, agent behavior, evaluation, deployment, and portfolio packaging.

What if I do not have a GitHub project?

Use an open-source project that interests you. The important part is learning with real code and real documentation.

Do I need an OpenAI API key?

Yes. The hands-on exercises use the OpenAI API. The course shows how to keep usage small while still building the full workflow.

Can I do it on a phone or tablet?

You can read the lessons anywhere, but you need a computer for the coding exercises.

Why is it free?

This course gives you a practical foundation and shows the teaching style we use at AI Shipping Labs. If you want more structure, feedback, and accountability after that, join the community and continue with sprints, workshops, and project reviews.