Generative AI for Software Engineering: Code Creation and Optimization

Lead your software teams through the AI revolution: Boost productivity, manage risks, and drive innovation.

Course Description:

Unlock the potential of Generative AI in software engineering for your teams. This intensive 2-day course is designed specifically for technology leaders, managers, and executives seeking to understand and leverage AI for enhanced productivity and innovation.

Explore the core concepts of AI-driven code creation and optimization, evaluate leading tools like GitHub Copilot and master essential prompt engineering techniques. Learn to critically assess AI-generated code quality , manage security risks , and strategically integrate these powerful tools into your development workflows. Equip yourself with the knowledge to guide your teams through the AI transformation, balancing speed with quality and navigating ethical considerations.

Who Should Attend:

• Head of Software Development
• IT Manager
• Project Manager
• Team Lead (Software Engineering / Development)
• Product Manager
• Executives (CTO, CIO, VP of Engineering) exploring AI adoption

Course Objectives:

Knowledge Acquisition:

• Understand the core concepts of Generative AI as applied to source code, including underlying models and the implications of their training data.
• Identify key GenAI tools and platforms for software engineering, comparing them from a strategic and managerial perspective.
• Recognize the critical interplay between productivity gains and potential risks like quality degradation and security vulnerabilities in AI-generated code.  
• Discuss the ethical considerations (bias, ownership, workforce impact) and licensing ambiguities associated with using GenAI in software development.
• Recognize emerging trends and the potential future trajectory of AI-driven software development.

Skills Development:

• Recognize effective prompt engineering techniques to guide AI models in generating, completing, and explaining code.
• Critically assess the quality, correctness, security, and maintainability risks associated with AI-generated code from a leadership standpoint.
• Analyze the capabilities and limitations of GenAI for various code creation (generation, completion, testing) and optimization tasks.
• Develop initial strategies for integrating GenAI tools into team workflows, considering adoption challenges and necessary oversight.

Practical Application:

• Evaluate the potential use of GenAI for code optimization, refactoring, bug detection, and documentation assistance within a team context.
• Understand how GenAI tools can accelerate routine coding tasks like function generation and boilerplate reduction.
• Assess the value and limitations of AI in automating aspects of unit test generation.
• Recognize how AI can be applied to explain complex code segments and assist in generating initial documentation drafts.

What will I Learn From it:

• Leverage AI to boost software team productivity and accelerate development cycles.
• Evaluate AI coding tools for strategic adoption within your engineering teams.
• Understand prompt techniques to guide AI for useful code and explanations.
• Identify and manage the quality and security risks of AI-generated code.
• Strategically integrate AI coding assistants into your team's development workflow.
• Assess the real impact of AI on software development productivity and quality.
• Navigate the complex ethical and licensing landscape of AI in software development.

Training Methodology

• Expert-Led Sessions: Clear explanations of core concepts and strategic considerations.
• Real-World Case Studies: Examining how companies are successfully (and unsuccessfully) leveraging AI in software development .
• Interactive Discussions: Opportunities to share challenges, insights, and experiences with peers and the instructor.
• Group Activities & Workshops: Collaborative exercises focused on evaluating AI outputs, crafting prompts, and developing initial integration strategies.  
• Tool Demonstrations: High-level showcases of leading AI coding assistants in action.

Requirement/
pre-requisites:

• No prior AI expertise is required.
• A general understanding of the software development lifecycle and common challenges faced by development teams is beneficial.
• Participants should be in or aspiring to leadership/management roles within a technology or software development context.