The 10x Engineer: Professional AI Certification

This course contains the use of artificial intelligence.

This professional AI certification course, "The 10x Engineer: Professional AI Certification," is designed to transform experienced engineers into masters of advanced software development through deep AI utilization and strategic adoption. Moving beyond basic tool usage, the curriculum focuses on customizing AI capabilities, maximizing efficiency, and implementing automated, end-to-end engineering workflows.

Mastering Context and Customization

Learners are equipped to achieve a deep codebase understanding across projects of any size. This involves leveraging tools like the Repo Info Agent to document the repository structure and the Multi-Repo Tool to enable agents to consult related codebases spread across multiple repositories.

A core focus is on personalizing AI tools to fit specific project needs. You will learn to control agent behavior by establishing custom Instructions and Rules and by creating specialized Custom Agents tailored with specific toolsets, which can then be shared across your organization. Furthermore, the course teaches you to integrate external applications using Custom Tools (MCPs), enabling complex use cases such as converting detailed Figma designs directly into production-ready code. Crucially, you will gain expertise in optimizing agent performance by strategically selecting the best LLM models (e.g., Mini 2.5 Pro, Sonnet 4.5, GPT-5 Codeex) based on the task, required speed, quality, and cost efficiency.

Implementing Strategic Development and Automation

You will master Spec-Driven Development (SDD), a technique that significantly improves agent outcomes by structuring the development around a detailed specifications document (including Requirements, Design, and Plan). The course demonstrates how to integrate SDD with established methodologies like Test-Driven Development (TDD) and Behavior-Driven Development (BDD), and how to use agents to assist in drafting and researching these specs.

The course culminates in implementing Autonomous Agents to automate entire end-to-end development cycles. This includes configuring complex flows using webhooks, Jira, and GitHub (or GitLab/Big Bucket) to automatically resolve assigned tasks, generate Pull Requests, and even configure secondary agents to autonomously review those generated PRs. This high level of automation is demonstrated through challenging use cases, such as directing a specialized agent to perform a complex code migration (e.g., Java 1.6 to Java 17).

Driving Organizational AI Adoption

Finally, the certification addresses the strategic challenge of driving AI adoption within an engineering organization, recognizing it as fundamentally a "people challenge," not a technology problem. Key strategies covered include:

1. Champion Programs: Identifying and investing heavily in a small group of peer experts who spread knowledge organically.

2. Communicating Successes: Consistently capturing and sharing specific, concrete wins (e.g., "Shara used AI to write 300 tests in two minutes") to build social proof and momentum.

3. Measurement: Utilizing usage dashboards to identify patterns, spot power users, and guide adoption efforts through informed conversations.

By the end of the course, you will have both the advanced technical expertise to build software at 10x speed and the strategic knowledge required to implement and scale AI across any engineering team.