Engineers and technical teams are in a unique position with AI. They often discover and adopt AI coding tools on their own — GitHub Copilot, Cursor, ChatGPT for debugging. But ad hoc adoption without structured training leads to inconsistent practices, security blind spots, and missed opportunities.
A structured AI course for engineers goes beyond "how to use Copilot" and covers the full spectrum: AI-assisted development, code review, automated testing, technical documentation, architecture support, and the critical governance layer around code security and intellectual property.
The technical audience also benefits from understanding AI at a deeper level than other roles. Engineers can leverage API-level prompt engineering, build internal AI tools and automations, and serve as AI technical advisors to their organisations. This makes engineering AI training fundamentally different from courses designed for non-technical teams.
Pertama Partners' CIRCUIT programme (AI for Technical Teams) is a 2-5 day programme designed for software engineers, DevOps professionals, QA engineers, and technical leads across Southeast Asia. It covers practical AI-assisted development skills alongside the security and governance knowledge that protects your codebase and your organisation.
A technical-depth introduction to how large language models work — going deeper than the business-level overview.
Hands-on training with the major AI coding tools, with emphasis on effective use patterns.
GitHub Copilot:
Cursor:
ChatGPT and Claude for development:
AI can serve as a preliminary code reviewer, catching common issues before human reviewers invest their time.
Sample workflow:
Test creation is one of the highest-value applications of AI for engineering teams.
| Testing Task | Without AI | With AI | Time Saved |
|---|---|---|---|
| Unit test suite for a module | 2-4 hours | 30-60 min | 70% |
| Integration test scaffold | 3-5 hours | 1-1.5 hours | 65% |
| Test data generation (100 records) | 1-2 hours | 10-15 min | 85% |
| Manual to automated test conversion | 4-6 hours | 1-2 hours | 65% |
AI assists with the configuration, scripting, and documentation tasks that consume DevOps engineering time.
Documentation is the task engineers most consistently avoid — and where AI provides the most welcome assistance.
| Documentation Task | Without AI | With AI | Time Saved |
|---|---|---|---|
| API endpoint documentation | 30-60 min per endpoint | 5-10 min per endpoint | 80% |
| README for a new project | 1-2 hours | 15-20 min | 85% |
| Architecture Decision Record | 1-2 hours | 20-30 min | 70% |
| Runbook for a service | 3-4 hours | 45-60 min | 75% |
AI can serve as a discussion partner for architectural decisions — not replacing the architect, but accelerating the analysis.
For engineers who want to build AI features into their products or internal tools.
Engineering AI governance is distinct from other departments — it involves code security, intellectual property, and open-source compliance.
| Governance Area | Rule | Rationale |
|---|---|---|
| Code security | Never input production credentials, API keys, or secrets into AI tools | Security breach risk — AI providers may log inputs |
| Proprietary code | Assess risk before inputting proprietary algorithms or business logic into public AI tools | Intellectual property protection |
| Open-source compliance | Review AI-generated code for potential licence contamination | AI may reproduce patterns from copyleft-licensed training data |
| Dependency security | Verify AI-suggested packages and dependencies before installation | AI may suggest deprecated, vulnerable, or non-existent packages |
| Production deployment | AI-generated code must pass the same review and testing standards as human-written code | Quality and security assurance |
| Data handling | Never use production data with AI tools for debugging or testing | Data protection compliance |
| Attribution | Document AI assistance in commit messages or code comments per team policy | Transparency and traceability |
IP and licence considerations for Southeast Asian companies:
| Task | Without AI | With AI | Time Saved |
|---|---|---|---|
| Boilerplate code generation | 30-60 min | 5-10 min | 85% |
| Unit test creation (per module) | 2-4 hours | 30-60 min | 70% |
| Bug diagnosis and fix | 1-3 hours | 20-45 min | 60% |
| Code refactoring | 2-4 hours | 45-90 min | 55% |
| Technical documentation | 3-4 hours | 45-60 min | 75% |
| DevOps configuration | 1-2 hours | 15-30 min | 70% |
| Code review (first pass) | 30-60 min | 10-15 min | 70% |
| Architecture evaluation | 4-6 hours | 1.5-2.5 hours | 55% |
| Tool | Engineering Use Case | Why It Matters |
|---|---|---|
| GitHub Copilot | Inline code completion, chat-based debugging, CLI assistance | Most widely adopted AI coding assistant; deep GitHub integration |
| Cursor | AI-first code editor, multi-file refactoring, codebase queries | Purpose-built for AI-assisted development; strong codebase awareness |
| ChatGPT | Complex debugging, architecture discussion, code translation | Versatile for open-ended technical discussions and multi-step problem solving |
| Claude | Code review, documentation, long-context analysis | Strong at analysing large codebases and producing detailed technical writing |
| Format | Duration | Best For | Group Size |
|---|---|---|---|
| Full Engineering AI Programme | 2 days (16 hours) | Complete engineering team upskilling | 10-20 |
| Development Focus | 1 day (8 hours) | Software engineers — coding, testing, review | 10-25 |
| DevOps Focus | 1 day (8 hours) | DevOps and infrastructure engineers | 10-20 |
| Tech Lead Programme | 1 day (8 hours) | Tech leads and engineering managers — governance + strategy | 5-15 |
| API Integration Workshop | Half day (4 hours) | Teams building AI features into products | 5-15 |
| Data Category | Can Use with AI | Conditions |
|---|---|---|
| Open-source code and public libraries | Yes | Standard development workflow |
| Internal boilerplate and templates | Yes | No embedded credentials or secrets |
| Architecture diagrams and design docs | Conditional | Remove sensitive infrastructure details |
| Production credentials and secrets | No | Absolute prohibition |
| Production data and customer data | No | Data protection compliance |
| Proprietary algorithms and core IP | Conditional | Risk assessment required; prefer enterprise AI tools |
| Metric | Before Training | After Training |
|---|---|---|
| Development velocity (story points per sprint) | Baseline | 30-50% increase |
| Test coverage | Baseline | 20-40% improvement |
| Documentation completeness | Often outdated | Current and comprehensive |
| Code review turnaround | 1-2 business days | Same day |
| Time on boilerplate and configuration | 25-30% of sprint | 10-15% of sprint |
| Bug diagnosis time | 1-3 hours average | 20-45 minutes average |
Is AI going to replace software engineers? No. AI is exceptionally good at generating boilerplate code, writing tests, creating documentation, and assisting with debugging. It is not good at understanding complex business requirements, making architectural trade-off decisions, or designing novel systems. The engineers who learn to use AI effectively will be significantly more productive than those who do not — but AI is an amplifier of engineering skill, not a replacement for it.
How do we handle intellectual property concerns with AI-generated code? The course dedicates a full governance module to this. Practical guidelines include: use enterprise versions of AI tools with appropriate data handling agreements, review AI-generated code for potential open-source licence contamination, treat AI code as a first draft requiring human review and modification, and document AI usage per your team's conventions. The legal landscape is evolving, and the course covers current best practices for Malaysia, Singapore, and Indonesia.
Should we use GitHub Copilot or Cursor? They serve different workflows. Copilot excels as an inline assistant within your existing IDE (VS Code, JetBrains). Cursor is an AI-first editor that provides deeper codebase awareness and multi-file editing capabilities. Many teams use both — Copilot for day-to-day coding and Cursor for larger refactoring and exploration tasks. The course covers both tools so your team can make an informed choice.
Can junior developers become too dependent on AI? This is a valid concern. The course addresses it directly: AI is most valuable when the engineer understands the code being generated. Junior developers should use AI to accelerate learning (explaining code, suggesting approaches, generating examples) rather than as a crutch that bypasses understanding. The course teaches techniques for using AI as a learning tool alongside its productivity benefits.
The course covers both software engineers (AI coding assistants, testing automation, DevOps) and other technical roles (AI for technical documentation, architecture review, project planning). Module depth is adjusted based on the team composition.
Yes. The course covers effective use of AI coding assistants (GitHub Copilot, Cursor, ChatGPT for code), including best practices for prompt engineering in code contexts, security considerations, and licence awareness for AI-generated code.
