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Fast replySignature workshopLast updated: Mar 29, 2026, 12:00

AI-Assisted Software Engineering: Practical, Secure & Production-Ready

This course is designed to help software engineers effectively leverage AI tools such as ChatGPT, GitHub Copilot, and Codex in real-world development workflows, while maintaining strong control over security, reliability, and code quality. Participants will engage in hands-on workshops covering prompt engineering, AI-assisted coding, debugging, testing, and secure development practices for production systems.

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Why This Course
Highlight 01

Training manuals and workshop materials.

Highlight 02

More than 70% of the course is workshop-based, with guided practice in real engineering scenarios.

Highlight 03

Participants will build and refine practical APIs and production-style features with AI assistance.

Why This Course

Why this course is worth learning

A quick overview of the key reasons this course works well for learners who want practical, job-ready skills.

Benefit 01

Hands-on by design

The course moves from principles to practical execution, so learners can apply it immediately after class.

Benefit 02

Structured for working teams

Topics are sequenced to reduce cognitive overload and help teams identify the most important takeaways quickly.

Benefit 03

A clearer decision path

Consultation and registration are placed at the right moments, creating a more confident conversion path.

Learning Outcomes

OBJECTIVES

A clear summary of what you will understand and be able to apply after the course.

6 learning goals
  • Apply AI in a structured software development workflow.
  • Write high-quality prompts for engineering tasks.
  • Review and improve AI-generated code.
  • Understand LLM limitations such as hallucination.
  • Identify and mitigate security risks.
  • Design secure AI-assisted development workflows.
Included

ALL PARTICIPANTS WILL RECEIVE

10 items
  • Certificate of completion for the AI-Assisted Software Engineering: Practical, Secure & Production-Ready course.
  • Training manuals and workshop materials.
  • Dedicated support and close guidance from instructors and staff throughout the course.
  • Hands-on experience with practical AI-assisted software engineering workflows.
  • A secure development mindset for reviewing, testing, and improving AI-generated code.
  • More than 70% of the course is workshop-based, with guided practice in real engineering scenarios.
  • Participants will build and refine practical APIs and production-style features with AI assistance.
  • Attack simulation exercises, including prompt injection scenarios, help teams understand AI security risks in practice.
  • A capstone project ties together prompting, coding, testing, review, and secure delivery in one production-oriented workflow.
  • Actionable patterns for introducing secure AI-assisted development practices into real teams and organizations.
Fit & Preparation

Who should join, and what should they prepare beforehand

See at a glance who this course is for and what background is recommended before joining.

Who Should Join

WHO SHOULD ATTEND?

  • Software engineers at all levels, from junior to senior.
  • Backend, frontend, and full-stack developers.
  • Tech leads and engineering managers.
  • DevOps and platform engineers.
  • Development teams already using tools such as ChatGPT or GitHub Copilot.
Prerequisites

PREREQUISITES

  • Basic programming experience, such as JavaScript, Python, or another language.
  • Foundational understanding of web or API development.
  • Basic familiarity with AI tools such as ChatGPT or Copilot.
Curriculum

OUTLINE

The curriculum is organized into modules so you can scan the overall structure first, then open the details you care about.

12 modules
01

AI in Software Engineering

4 topics inside this module

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  • AI across the software development lifecycle.
  • How LLMs work: tokens, context, and prediction.
  • LLM limitations such as hallucination and pattern-based reasoning.
  • Vibe coding vs. structured AI-assisted development.
02

Prompt Engineering for Real Work

4 topics inside this module

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  • Building prompts with context, constraints, and output format.
  • Techniques for controlling AI output quality.
  • Comparing low-quality prompts with high-quality prompts.
  • Workshop: rewrite prompts for the same engineering task and compare results.
03

Structured AI-Assisted Development Workflow

4 topics inside this module

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  • Requirement to design to code to review to test to deploy.
  • Trust boundaries between human judgment and AI output.
  • Role separation between engineers and AI tools.
  • Mini project: generate a REST API with AI and refine it for real use.
04

AI Code Review and Debugging

4 topics inside this module

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  • Reviewing AI-generated code for logic correctness.
  • Finding edge cases and weak error handling.
  • Debugging defects introduced by generated code.
  • Workshop: analyze buggy and vulnerable code, then improve it.
05

Testing with AI

4 topics inside this module

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  • Using AI to generate unit tests.
  • Designing edge cases and negative test scenarios.
  • Evaluating test quality and coverage.
  • Workshop: create tests for code developed during class.
06

Integrated Development Practice

4 topics inside this module

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  • Add a new feature with AI assistance.
  • Improve validation and error handling.
  • Expand automated test coverage.
  • Refine implementation quality before release.
07

Security Risks in AI-Generated Code

4 topics inside this module

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  • Security concerns related to AI-assisted software development.
  • OWASP-relevant risks in generated code.
  • SQL Injection, XSS, and broken authentication.
  • Secret leakage, data exposure, and dependency risk.
08

Vulnerability Detection Workshop

4 topics inside this module

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  • Inspect insecure code samples.
  • Identify technical and security risks.
  • Fix vulnerable implementations.
  • Validate safer coding outcomes.
09

AI Attack Simulation

4 topics inside this module

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  • Prompt injection fundamentals.
  • Jailbreak techniques and bypass attempts.
  • Attacker vs. defender simulation.
  • Learning how to reduce unsafe AI behavior.
10

Designing a Secure AI Workflow

4 topics inside this module

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  • Prompt design with safer guardrails.
  • Context control and data handling boundaries.
  • Validating AI outputs before use.
  • Defining AI usage policy for teams and organizations.
11

Production Capstone Project

4 topics inside this module

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  • Build a production-style feature using AI.
  • Apply input validation and authentication or authorization.
  • Include unit tests and secure code structure.
  • Prepare the feature for final review and presentation.
12

Presentation and Best Practices

4 topics inside this module

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  • Present project outcomes.
  • Receive instructor feedback.
  • Summarize secure and practical AI engineering practices.
  • Create a path for adoption in real teams.
Schedule & Registration

Public Training Schedule

Review upcoming sessions, key logistics, and registration details in one place.

Available Sessions

No schedule is currently available for the public training course.

Need a private session or special arrangement? Contact us.