AI Training Academies • AI-Powered Software Engineer

Master AI-Powered Software Engineering

A 15-day intensive program that builds durable AI engineering habits—from prompting and IDE assistants to agentic coding tools—and closes with a real capstone feature shipped with agents, tests, and AI-augmented review.

15
Training Days
3
Weeks
3–6
Weeks Flexible

Schedule options: Full-time (3 weeks, Mon–Fri) · Half-days (~6 weeks) · Custom cadence for global teams

Request Training Quote View Curriculum
Claude CodeClaude Code
GitHub CopilotGitHub Copilot
GitGit
GitHubGitHub
IDEs & Editors
VS CodeVS Code
CursorCursor
WindsurfWindsurf
Visual StudioVisual Studio
JetBrains IDEsJetBrains
ZedZed
Customer-Pickable Backend Stack
JavaJava
PythonPython
TypeScript/NodeNode/TS
C#C#
RustRust

Build Real AI Engineering Habits, Not Just Tool Familiarity

AI assistants accelerate development only when used with discipline. This academy goes beyond demos to build the prompting instincts, review habits, security awareness, and agentic workflows that distinguish effective AI engineers from those who accumulate AI-generated technical debt.

  • Expert Instructors
    Practitioners with real-world AI engineering experience
  • Customized Curriculum
    Stack, tools, and capstone adapted to your team
  • Hands-On Labs
    Real tasks throughout; real capstone in week 3
  • Flexible Scheduling
    Full days, half-days, or custom cadence
  • Online or On-Site
    Delivered wherever your team works
  • Session Recordings
    Review material at your own pace (online delivery)
  • 3–20 Cohort
    Focused cohort size for effective live instruction
  • AI-Off Variant Available
    Policy-compliant version on request
Week 1: Foundations

Prompting and context engineering, frontier models, AI-assisted SE habits and review discipline

Week 2: Tools and Agents

IDE assistants (Copilot, Cursor, Windsurf) and terminal agents (Claude Code, Codex, Aider) with comparative labs and GitHub Spec Kit's spec-driven workflow

Week 3: Capstone and Guardrails

Ship a real feature with agents, tests, and AI-augmented review; security, IP, and team policy guardrails

Academy Curriculum

Three weeks covering foundations, tools and agents, and a real capstone with guardrails.

Week1

Foundations of AI-Assisted Engineering

5 full-days or 10 half-days · 35 training hours

Build the mental models and habits that make AI assistance reliable rather than risky. Understand how frontier models process context, develop strong prompting instincts, and establish the review discipline and team workflows that sustain quality.

Topics

  • Context windows, token budgets, and attention
  • System prompts, few-shot, and chain-of-thought
  • Structured outputs and tool definitions
  • Frontier model comparison (Claude, GPT, Gemini)
  • Code review discipline for AI-generated output
  • Security pitfalls and when to disable AI
  • Team workflows, prompt libraries, instruction files

Lab Project

Build and validate a prompt library and team instruction file for a realistic engineering scenario in the customer's chosen stack.

Skills Gained

  • Write prompts that produce consistent, reliable output
  • Review AI-generated code with a structured checklist
  • Choose the right model for a given task
  • Design team AI usage standards
Week2

IDE Assistants and Terminal Agents

5 full-days or 10 half-days · 35 training hours

Hands-on comparative coverage of the leading AI IDEs (GitHub Copilot, Cursor, Windsurf) and terminal-based agentic coding tools (Claude Code, Codex, Aider). Participants run identical engineering tasks in each tool and develop informed judgment about when and how to use each.

Topics

  • GitHub Copilot: chat, edits, agents, spaces, MCP
  • Cursor: Agent (formerly Composer), Tab, rules files, context
  • Windsurf: Cascade agent, workspace config
  • Claude Code: CLAUDE.md, hooks, custom tools, subagents
  • OpenAI Codex: CLI, IDE extension, cloud agents, and the GPT-5.x Codex model family
  • Aider: Git-native, repository map, commit workflow
  • GitHub Spec Kit: spec-driven workflow across all of the above
  • Agentic code review and PR automation
  • Tool selection framework

Lab Project

Complete identical feature implementation and refactoring tasks across multiple tools; evaluate and document findings in a team tool-selection scorecard.

Skills Gained

  • Use each major AI IDE at a professional level
  • Drive terminal agents with CLAUDE.md and hooks
  • Configure agentic code review in CI
  • Make principled tool selection decisions
Week3

Capstone and Guardrails

5 full-days or 10 half-days · 35 training hours

Ship a real feature end-to-end using the tools and habits from weeks 1–2, then close with the security, IP, and team-policy guardrails every AI-empowered engineering org needs.

Topics

  • Capstone: feature implementation with agents
  • AI-driven test generation for the capstone
  • Agentic PR automation and code review
  • Prompt injection and secret leakage defense
  • AI-generated code copyright and licensing
  • Team policy design: acceptable use, review gates
  • Rollout strategy and change management

Capstone Project

Ship a customer-specified feature using agentic tools, with an AI-generated test suite, automated PR review in CI, and a team acceptable-use policy document.

Skills Gained

  • Ship production-quality features with agent assistance
  • Generate and validate tests with AI
  • Apply security and IP guardrails to AI workflows
  • Design and communicate team AI policy

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