AI Engineering refers to the industry-relevant skills that data science and engineering teams need to successfully build, deploy, operate, and improve Large Language Model (LLM) applications in production environments.

In practice, as of the Fall of 2025, this requires understanding both prototyping and production deployments in the following ways.

During the prototyping phase, Prompt Engineering, Retrieval Augmented Generation (RAG), Agents, and Fine-Tuning are all necessary tools to be able to understand and leverage. Prototyping includes:

1. Building RAG Applications
2. Building with Agent and Multi-Agent Frameworks
3. Deploying LLM Prototype Applications to Users

When productionizing LLM application prototypes, there are many important aspects ensuring helpful, harmless, honest, reliable, and scalable solutions for your customers or stakeholders. Productionizing includes:

1. Evaluating RAG and Agent Applications
2. Improving Search and Retrieval Pipelines for Production
3. Improving Agent and Multi-Agent Applications
4. Monitoring Production KPIs for LLM Applications
5. Setting up Production Endpoints for Open-Source LLMs and Embedding Models
6. Building LLM Applications with Scalable, Production-Grade Components
7. Understanding and Building with Agent Protocols

The AI Engineering Bootcamp is an ever-evolving course that keeps pace with the industry.

If you're serious about becoming an AI-Assisted developer, you're in the right place.

With that, it's time to jump in and 🛣️ Get Started.

To become AI-Makerspace Certified, which will open you up to additional opportunities for full and part-time work within our community and network, you must:

1. Complete all required project assignments, including weekly videos (Weeks 1-5, 7-8)
2. Complete the Certification Challenge, including a Demo video (Week 6)
3. Compete with other cohort members with a live pitch and Demd of your project during Demo Day Semifinals (November 11, 2025)
4. If you are selected for Demo Day by your peers, you must present live on November 13, 2025. Otherwise, you will be required to submit your own YouTube-worthy Demo video
5. Receive at least an 85% total grade in the course.

If you do not complete the above requirements or maintain a high-quality standard of work, you may still be eligible for a certificate of completion if you miss no more than 2 live sessions.

This GitHub repository is your gateway to mastering the art of AI Engineering. All assignments for the course will be released here for your building, shipping, and sharing adventures!

We believe in the power of collaboration. Contributions, ideas, and feedback are highly encouraged! Let's build the ultimate resource for AI Engineering together.

Please to reach out with any questions or suggestions.

Happy coding! 🚀🚀🚀