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Featured modules from the AI-assisted engineering track.
Foundations: Modern SDLC + the AI-Augmented Engineer
Foundations: Modern SDLC + the AI-Augmented Engineer
A grounding module. We define the modern SDLC (Plan → Design → Develop → Test → Release → Operate → Learn), show where AI augmentation slots in, and reset expectations about what an engineer's day looks like when an AI assistant is part of the workflow. By the end, every learner has a one-page diagram of an SDLC for a real product and a written list of where AI would or would not help.
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AI-Assisted Development & Vibe Coding
AI-Assisted Development & Vibe Coding
The signature module of the program. Learners spend most of the session **doing**, not listening. We tour the major AI coding tools (Cursor, GitHub Copilot, Claude Code, Aider), introduce four prompt patterns, then run a debug lab where each learner fixes a broken FastAPI endpoint using AI as a pair-programmer and documents every prompt. The point is to make AI a habit, not a curiosity.
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AI-Augmented Workflows end-to-end
AI-Augmented Workflows end-to-end
The capstone of the AI-augmented track. We take a small feature from request → branch → code → tests → PR → review → deploy → observe, with AI involved at every phase and a single prompt log as the spine. Then we triage a production incident with AI as a triage partner, while protecting secrets and avoiding fabrication risks. The deliverable is an **evidence pack** that future reviewers (or hiring managers) can…
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Visual AI Builders: Idea to MVP
Visual AI Builders: Idea to MVP
This module teaches the “vibe MVP” loop: start with a clear problem, get to a working demo fast using a visual AI builder, and keep quality by adding constraints, checks, and a small evidence log. The aim is not perfect architecture — it is a usable prototype with a credible story for how it works and what it doesn’t do yet.
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