About the Course
Software leaders increasingly want results they can prove in engineering practice, not just experiments with chatbots. In this domain, you need to demonstrate AI-assisted coding fluency, prompt discipline, code review judgment, test generation skill, secure use of model outputs, and release governance aligned with established engineering controls such as ISO/IEC/IEEE 12207 and OWASP guidance. Generative AI for software development and engineering teams gives you a structured way to move from ad hoc prompting to repeatable use cases that support delivery quality and speed.
The course turns scattered experimentation into a working system for software delivery. You will practice prompt design for code generation, test case creation, debugging support, refactoring suggestions, documentation drafting, and engineering knowledge capture. You will also be introduced to model-risk controls, code review guardrails, and team workflows that support tools such as ChatGPT, GitHub Copilot, and similar coding assistants, while keeping implementation honest about what AI can and cannot do in a real development team. What you will learn: you will learn how to use generative AI to accelerate coding, testing, and documentation while keeping review, security, and quality controls in place. You will practice building prompts, reviewing AI-generated code, and drafting team guidance; you will be introduced to governance patterns for responsible adoption at team level.
The course is built for teams that face delivery pressure, legacy code complexity, uneven AI literacy, and constant scrutiny over software quality. It is especially relevant where engineering groups must collaborate across product, security, QA, and operations while adopting AI tools without creating unacceptable risk. The approach assumes realistic constraints such as limited time for experimentation, mixed tool maturity, and the need to prove value quickly through tangible outputs rather than abstract innovation claims. This course teaches generative AI for software development through applied labs so you can ship practical workflows instead of isolated demos.
Target Audience
This course is designed for engineering professionals who need practical, team-ready ways to use generative AI in software delivery, testing, and code governance.
- Software Engineers who want to draft, refactor, and review AI-assisted code safely.
- Senior Developers who need to standardize prompt patterns across a feature team.
- QA Automation Engineers who generate test cases and edge-condition checks with LLMs.
- DevOps Engineers who apply generative AI to release notes and deployment support.
- Engineering Managers who need repeatable AI use cases for delivery teams.
- Technical Leads responsible for code quality, review discipline, and team adoption.
- Product Engineers who bridge product requirements into faster implementation workflows.
- Platform Engineers who document internal tooling and reusable developer guidance.
- Security Engineers who evaluate AI-assisted coding risks and prompt leakage.
- Software Architecture Leads who assess where generative AI fits in design and review.
Course Objectives
This course equips you to plan, execute, and measure generative AI for software development initiatives that accelerate delivery, strengthen engineering controls, and support responsible team adoption.
- Assess your current software delivery workflow using a Generative AI readiness checklist and code review map.
- Apply prompt engineering techniques to generate code, tests, and documentation in realistic engineering tasks.
- Design reusable prompt templates for debugging, refactoring, and API integration support.
- Build an AI-assisted test generation workflow for unit and edge-case coverage.
- Calculate quality impacts using defect density, test coverage, and review rework metrics.
- Evaluate AI-generated code against OWASP guidance, secure coding rules, and team standards.
- Implement usage guardrails for Copilot- or ChatGPT-supported development in a team setting.
- Synthesize adoption findings into an engineering playbook, rollout plan, and stakeholder update.
Requirements & Prerequisites
Intermediate familiarity with software development concepts is required, including source control, debugging, code review, and basic testing practices. You should have working knowledge of at least one programming language used in your team, but you do not need advanced machine learning experience. Coding/programming is not required for course completion beyond reading, editing, and evaluating sample code, although hands-on lab work will ask you to use prompts and review AI-generated outputs. Advanced concepts such as model governance and secure usage patterns are taught at the operational application level, not at production engineering depth. A laptop with browser access is recommended.
Local Application and Business Return
How participants can apply the training in local operating conditions, and the return their organisation can plan for.
How participants apply this
Expected ROI
Training Methodology
This is a practical, outcome-driven course designed to turn generative AI for software development aspiration into measurable action and credible reporting.
Methodology includes:
- Hands-on prompt lab using a code-generation dataset and defect-fix task.
- Scenario simulation for a production bug triage with AI-assisted debugging.
- Assessment using an AI code review checklist and secure coding rubric.
- Stakeholder mapping for engineering, QA, security, and product approval flow.
- Case analysis from fintech, SaaS, healthcare software, and enterprise IT teams.
- Group workshop producing a prompt library and AI coding guardrail draft.
- Reflection exercise against benchmarked review time, defect rate, and test coverage data.
Upcoming Sessions
Next available dates worldwide
Certification
Recognized credentials that advance your career
Participants who complete the Generative AI for Software Development and Engineering Teams Training Program earn a Trainingcred Certificate of Achievement, demonstrating professional competence and alignment with global standards in learning and development.
NITA Accredited
Accredited by the National Industrial Training Authority, ensuring programs meet nationally recognized standards of quality and relevance.
CPD Certified
Recognized by the CPD Certification Service, ensuring every program meets internationally benchmarked standards of professional excellence.
Why this course earns its place on your CV
Accredited training, practitioner trainers, and peers on the same career track — the three things real expertise is built on.
Effective Learning & Skill Development
- Build expertise with structured, outcome-driven learning.
- Equip individuals and teams with skills that grow with industry needs.
- Reinforce learning through real-world scenarios, case studies and practical exercises.
Career Growth & Professional Advancement
- Apply what you learn with a proven methodology that ensures lasting impact.
- Develop immediately usable skills that translate directly into workplace success.
- Gain the expertise needed for career advancement and leadership roles.
Training Optimization & Learning Excellence
- Tailor training to industry-specific challenges and organizational goals.
- Use data-driven insights and automation to enhance training effectiveness.
- Evaluate progress and ensure long-term learning success.
Tools and platforms relevant to this field
Examples Thailand teams may encounter, and that may be featured in training where they support the confirmed course scope.
These are field-relevant examples, not a promise that every tool will be covered. Exact coverage depends on the confirmed course scope, participant needs, and delivery format.
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GitHub Copilot GitHubUsed by software teams to generate code suggestions, accelerate refactoring, and support test and documentation drafting in everyday development workflows.
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Visual Studio Code MicrosoftCommon development environment for applying AI-assisted coding, reviewing generated code, and integrating extensions such as coding assistants into team workflows.
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ChatGPT OpenAIUsed for prompt-based code explanation, test idea generation, debugging support, and draft documentation during software delivery.
