AI Deep Learning Program

AI Deep Learning Program Course Details

The AI Deep Learning Program is built on a simple premise: AI skills only stick when teams build them into real work. The program pairs a senior trainer — who delivers six half-day sessions introducing techniques, establishing shared vocabulary, and building core skills — with a dedicated Embedded Engineer who works alongside your team every day between sessions, applying concepts to real projects, building habits, and extracting reusable skill files from actual project context.

The first six weeks build the foundation. Your team creates working skill files for Acceptance Criteria generation, unit test generation, and test planning. Each session produces a committed artifact your team uses immediately, not a concept they study later. The second six weeks make it rigorous. Participants learn to evaluate their own output, raise their skill files to a transferable standard, and build their first bounded agents and workflows.

Three roles participate together throughout the entire program — Product/Process, Development, and Testing — because the artifact chain from Acceptance Criteria to Tests to Test Plans only works when everyone understands every link.

AI Deep Learning Program Learning Objectives

Loop 1: Build the Foundation (Weeks 1–6)

Session 1: Acceptance Criteria Generation

• Understand what makes Acceptance Criteria specific, testable, and AI-consumable

• Build a working skill file that generates AC from real backlog stories

• Commit the skill file to your repository and use it with the Embedded Engineer the next day

Session 2: Test Generation from Acceptance Criteria

• Generate unit tests directly from stories and Acceptance Criteria using a skill file

• Understand how AC quality drives test quality

• Produce a test generation skill file and integrate it into your workflow

Session 3: Test Plan Generation

• Assemble full coverage plans from Acceptance Criteria and generated tests

• Create a test plan skill file that connects the full chain

• Complete the AC → Tests → Test Plans artifact pipeline

Loop 2: Make It Rigorous (Weeks 7–12)

Session 4: Evaluating AI Output

• Build evaluators that measure skill file output quality with structured frameworks

• Learn to identify failure modes and systematically improve skill file performance

• Target at least one workflow step at 95%+ accuracy

Session 5: Raising the Bar — Peer Review and Transferability

• Peer-review skill files across the team for consistency and clarity

• Raise skill files to a transferable standard any team member can use and get consistent results

• Establish team conventions for skill file maintenance and versioning

Session 6: Bounded Agents and Workflows

• Chain validated skill files into bounded agents and repeatable workflows

• Define governance boundaries and failure-handling strategies

• Plan ongoing adoption and integration into existing team processes

Who is the AI Deep Learning Program For?

• Cross-functional software teams ready to embed AI into their daily workflow, not just experiment with it

• Development teams that want to move beyond ad-hoc prompting to structured, repeatable AI-assisted processes

• Product Owners and Business Analysts who define Acceptance Criteria and want AI to amplify their output

• QA and Testing professionals seeking to generate tests and test plans from structured inputs

• Engineering leads evaluating how to roll out AI tooling across their organization with measurable results

• Teams that have tried AI coding tools but haven't seen consistent, reliable output

Prerequisites

• Each participant should have a laptop with an IDE and access to at least one AI coding tool (Claude Code, Cursor, Windsurf, GitHub Copilot, or similar)

• An active codebase or project the team is working on (artifacts are built against real work, not exercises)

• No prior experience with AI skill files, evaluators, or agentic workflows is required

What You Leave With

• Working Acceptance Criteria, test generation, and test plan skill files committed to your repository

• Evaluators that measure and improve your skill file output quality

• Peer-reviewed, transferable skill files any team member can use and get consistent results

• At least one workflow step validated at 95%+ accuracy — reliable enough to trust

• Bounded agents and workflows that chain skills into repeatable, governed automation

• A daily practice established with the Embedded Engineer that outlasts the program