AI in
Software Engineering
Software Engineering
An advanced practical program for senior developers, lead engineers, QA automation engineers, technical product leads, and engineering teams who want to move beyond basic AI-assisted coding and build structured, scalable, and production-aware engineering workflows with AI.
What is this course
A STRATEGIC UPGRADE
YOU ALREADY KNOW SOFTWARE ENGINEERING.
NOW LEARN HOW AI CHANGES IT.
NOW LEARN HOW AI CHANGES IT.
01
02
03
Understand AI deeply
Apply it strategically
Build a system
Learn how AI is changing software delivery, developer roles, team workflows, and engineering expectations - not as a shortcut, but as a new engineering capability.
Use AI to support planning, implementation, testing, review, documentation, debugging, and decision-making while keeping human technical ownership.
Create AI-assisted workflows with clear specifications, context architecture, quality checks, security rules, governance, and human approval gates.
In 8 sessions, you’ll learn how AI is changing software delivery, how to prepare repositories for AI, write better specifications, work with coding agents, review AI-generated code, improve testing workflows, and build scalable AI-native engineering systems.
Who want to move beyond basic AI-assisted coding and use AI to improve real production workflows without losing code quality, maintainability, or architectural control.
Senior Developers
Technical Product Leads
Who need to turn vague tickets, product requirements, and business needs into AI-readable technical specifications with clear constraints and acceptance criteria.
Lead Engineers
Who define engineering standards, review processes, technical direction, and need to introduce AI into development workflows in a structured way.
Engineering Teams
Who want to build a shared AI-assisted operating model instead of using AI randomly across individuals and tools.
QA Automation Engineers
Who want to apply AI to test strategy, unit/integration/E2E coverage, edge-case discovery, flaky test analysis, stack trace interpretation, and QA automation.
Engineering Managers
Who want to introduce AI into teams responsibly, with governance, security rules, quality metrics, cost control, and long-term adoption strategy.
Who is this for
BUILT FOR MODERN ENGINEERING TEAMS
WHO WANT TO STAY RELEVANT
WHO WANT TO STAY RELEVANT
AI Review Systems
05
The AI Engineering Stack
04
Agentic Engineering
The AI Engineering Operating Model
07
08
Engineering Reliability
06
Specification-Driven Engineering
03
Engineering Memory
02
The New Engineering Reality
01
Curriculum
EIGHT SKILLS.
ONE MONTH.
ONE MONTH.
Final Project
YOU DON’T GRADUATE WITH A TOOL LIST.
YOU GRADUATE WITH AN AI ENGINEERING OPERATING MODEL.
YOU GRADUATE WITH AN AI ENGINEERING OPERATING MODEL.
01
02
03
AI-Ready Repository Map
Context System
AI Engineering Tool Stack
A structured repository setup showing documentation, architecture notes, coding standards, test rules, API contracts, decision records, and AI instruction files.
A reusable specification framework with acceptance criteria, constraints, non-goals, implementation boundaries, risk areas, validation rules, and architectural guardrails.
A practical tool stack with coding assistants, repo-aware agents, IDE tools, cloud agents, review workflows, testing support, and documentation workflows.
04
Reliability Workflow
A structured workflow for pull request review, risk analysis, missing test detection, regression checks, QA automation, debugging, log reasoning, and incident analysis.
05
Team AI Governance Policy
A practical one-page AI use policy for engineering teams, including acceptable use, prohibited use, security rules, human approval gates, evaluation standards, cost control, and quality metrics.
In the final session, you’ll present a practical AI-assisted engineering workflow designed for a real software development team or engineering use case.
Add your details and proceed to payment.
Secure your spot now
Start your journey at ACA X
Course Fee: 100,000 AMD
Get up to 100,000 AMD back if you submit your annual tax declaration and have income tax.
Master AI skills.
Build your future.
Lead the change.
Build your future.
Lead the change.
X
About ACA
ACA X IS BACKED
BY 10 YEARS OF
TECH EDUCATION
BY 10 YEARS OF
TECH EDUCATION
ACAx is the professional AI direction of Armenian Code Academy - one of Armenia's most established tech education institutions, founded in 2015.
For a decade, ACA has trained engineers, designers, product managers, and data professionals who now work at global companies, local unicorns, and fast-growing startups. ACAx is the next evolution -professional AI skills for everyone, not just engineers.
For a decade, ACA has trained engineers, designers, product managers, and data professionals who now work at global companies, local unicorns, and fast-growing startups. ACAx is the next evolution -professional AI skills for everyone, not just engineers.
10+
YEARS IN TECH EDUCATION
17K+
TOTAL GRADUATES
PARTNER COMPANIES
100+
300+
TUTORS
FREQUENTLY ASKED QUESTIONS
This course is designed for senior developers, lead engineers, QA automation engineers, technical product leads, engineering managers, and software engineering teams who want to use AI in real software development workflows.
Basic familiarity with AI tools is helpful. This is not a beginner prompting course — it is an advanced practical program for technical professionals who want to apply AI across engineering workflows.
No. The course goes beyond coding. It covers AI-ready repositories, specifications, context architecture, coding agents, AI review systems, testing workflows, engineering reliability, internal agents, and team-level governance.
The course introduces modern AI engineering tools such as Claude Code, Cursor, GitHub Copilot, Codex, Gemini, ChatGPT, and other coding agents or repo-aware tools.
You’ll be able to design AI-assisted engineering workflows, prepare repositories for AI, write precise specifications, work with coding agents, evaluate generated code, improve testing and debugging workflows, design internal engineering agents, and adopt AI safely at team level.