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Overview
Artificial intelligence is transforming how software engineers design, build, test, and maintain applications. This course introduces practical approaches to using generative AI as a development partner across the software engineering lifecycle.
Participants explore how modern AI assistants can accelerate coding, debugging, documentation, and problem solving when used effectively. The course focuses heavily on prompting techniques that help engineers produce reliable, structured outputs from AI tools.
Through guided demonstrations, collaborative activities, and real-world scenarios, learners practise writing prompts that generate useful results for common engineering tasks. They also learn how to evaluate AI responses critically and refine prompts through structured iteration.
By the end of the course, participants will be able to integrate AI assistants into their daily development workflow and confidently apply advanced prompting techniques to improve software engineering productivity.
Prerequisites
Participants should have:
- Basic software engineering experience, including familiarity with programming concepts and development workflows
- Experience working with an integrated development environment such as Visual Studio Code
- A general understanding of modern software development practices such as debugging, documentation, and testing
- Interest in exploring how AI tools can support development productivity and collaboration
Target audience
This course is designed for:
- Software engineers and developers who want to incorporate AI tools into their development workflow
- Engineering teams exploring generative AI to improve productivity and development efficiency
- Technical leads and architects interested in understanding the capabilities and limitations of AI-assisted development
- DevOps engineers and technical professionals who collaborate closely with development teams
Learning objectives
By the end of this course, learners will be able to:
- Identify practical and high-impact use cases for generative AI in software engineering workflows
- Explain the role of human oversight when working with AI systems and describe how AI can act as a thought partner for technical experts
- Configure and use an AI coding assistant within a development environment such as Visual Studio Code
- Evaluate AI generated responses and determine when refinement or human validation is required
- Apply advanced prompting principles including clarity, context, constraints, and role instructions
- Use structured prompting techniques such as Persona, Task, Context, and Format to guide AI outputs
- Iteratively refine prompts to improve the quality, relevance, and usefulness of generated results
- Design effective prompts for tasks such as code generation, debugging, documentation, and architectural exploration
Course Outline
Introduction to AI for software engineers
Understanding how AI is reshaping software development practices
- Overview of generative AI and large language models
- How AI tools support software engineering tasks
- Common use cases across the development lifecycle
- Opportunities and limitations of AI assisted development
Human and AI collaboration in engineering
- Why human oversight remains essential
- Using AI as a thinking partner for engineers
- Risks such as hallucinations, bias, and incorrect outputs
- Strategies for validating AI generated results
Quick fire prompting: use case proof of concept
Rapid exploration of AI capabilities through practical examples
- Demonstrating how prompts influence AI responses
- Exploring simple prompts for coding and engineering tasks
- Testing AI for documentation, debugging, and refactoring scenarios
- Working in teams to explore real development problems using AI
Evaluating AI outputs
- Recognising useful versus low quality responses
- Understanding when prompts require refinement
- Identifying patterns in successful AI interactions
Advanced prompting principles
Developing prompts that produce consistent and high quality outputs
- The importance of clarity in prompt design
- Providing context for more accurate responses
- Applying constraints to guide output structure
- Using role based instructions to shape AI behaviour
Structuring prompts for engineering tasks
- Understanding the Persona, Task, Context, and Format framework
- Creating prompts that reflect real engineering scenarios
- Designing prompts that produce structured technical output
Advanced prompting principles activity
Hands on practice applying structured prompting techniques
- Analysing weak prompts and identifying improvements
- Revising prompts using clarity, context, and constraints
- Testing revised prompts and comparing results
- Collaborative peer feedback on prompt design
Prompting techniques for complex tasks
Using advanced prompting strategies for multi step engineering work
- Breaking complex problems into smaller prompt driven tasks
- Guiding reasoning through step by step prompts
- Using prompts for architecture exploration and problem solving
- Managing longer AI conversations for complex outputs
Iterative prompt development
- Testing prompt variations and evaluating responses
- Identifying prompt patterns that improve consistency
- Using feedback loops to refine prompts over time
Prompting techniques for complex tasks – demonstration and deep dive
Exploring advanced prompting approaches through instructor led demonstrations
- Demonstrating prompts for debugging complex code
- Using AI to generate test cases and documentation
- Applying prompts for code refactoring and optimisation
- Analysing how prompt structure changes AI output quality
Mastering prompting for software engineers
Applying all techniques in real world engineering scenarios
- Designing prompts for multiple software engineering tasks
- Strengthening weak or ambiguous prompts through structured revision
- Developing high quality prompts for new development use cases
- Collaboratively evaluating prompts and refining them to mastery level
Building a personal AI enabled engineering workflow
- Identifying daily development tasks suitable for AI assistance
- Creating reusable prompt patterns for engineering work
- Developing a personal strategy for responsible AI usage
- Planning how to integrate generative AI into day to day development practices
Exams and assessments
There are no formal exams included in this course.
Participants complete practical activities, collaborative exercises, and instructor guided discussions throughout the course. These activities help reinforce key concepts such as prompt design, AI output evaluation, and iterative refinement techniques.
Hands-on learning
This course includes:
- Guided demonstrations of AI assisted development workflows
- Collaborative group activities exploring real software engineering scenarios
- Practical exercises designing and refining prompts for engineering tasks
- Instructor supported experimentation with AI tools in a development environment
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