Software teams are increasingly exploring technologies that can assist with coding, documentation, testing and code analysis. While these capabilities can improve productivity, they also raise important questions around code quality, validation and responsible usage within development environments.
This two-day technical workshop explores practical approaches for integrating AI-assisted capabilities into development workflows while maintaining professional engineering standards.
Participants explore examples showing how these tools can support activities such as analysing existing repositories, generating documentation, identifying potential issues and assisting with testing and debugging.
The workshop also explores modern AI coding assistants such as GitHub Copilot and similar development tools
Topics explored during the course include:
• understanding how AI capabilities are influencing modern software development practices
• structuring prompts for development and coding tasks
• generating documentation and summaries from code repositories
• analysing code to identify potential defects and improvements
• supporting testing and debugging activities
• introducing AI capabilities responsibly within engineering environments
The workshop combines instructor-led explanation with practical demonstrations, allowing participants to see how these approaches can be applied within real development scenarios.
By the end of the course participants will have a clearer understanding of how AI-assisted capabilities can support developer productivity while maintaining quality and control across development activities.
Course OutlineDay 1 – AI in Modern Software DevelopmentModule 1 – AI in the Software Development Lifecycle
• how AI technologies are influencing software engineering practices
• identifying where AI can support coding, analysis and documentation
• understanding capabilities and limitations of AI-generated output
Module 2 – Prompt Engineering for Development Tasks
• structuring prompts for coding and development activities
• refining prompts to improve code generation and analysis
• managing context and instructions for reliable outputs
Module 3 – Repository Analysis and Documentation
• analysing existing repositories and codebases
• generating documentation from source code
• summarising complex code structures and components
Day 2 – Applying AI in Engineering WorkflowsModule 4 – Supporting Testing and Debugging
• exploring how AI can assist with test creation and validation
• analysing code behaviour and identifying potential defects
• reviewing outputs to maintain code quality standards
Module 5 – Reviewing and Validating AI-Generated Code
• evaluating AI-generated code for correctness and reliability
• identifying hallucinations, security issues and logical errors
• validating outputs against existing coding standards and architecture
• integrating AI-generated suggestions into code review processes
• maintaining quality and engineering oversight
Module 6 – AI-Assisted Development Workflows
• integrating AI into development and collaboration workflows
• supporting engineering teams with analysis and documentation tasks
• examples of productivity improvements in development environments
Module 7 – Responsible Use in Engineering Environments
• approaches for validating AI-generated outputs
• understanding risks associated with automated suggestions
• maintaining engineering standards and governance
Also Available for Teams
This course is also available as private training for software development teams and organisations.
Private delivery allows the workshop to be adapted to specific technology stacks, development environments and engineering practices.
This format works particularly well for teams exploring how AI capabilities can support developer productivity while maintaining quality and oversight across development workflows.
Participant Feedback
Professionals who have attended similar workshops have highlighted the practical nature of the session and the focus on real development environments.
"It helped our team understand how these tools could support productivity without compromising engineering standards."
Engineering Manager
"The balance between productivity and engineering discipline was exactly what we needed when exploring AI in development."
Head of Software Engineering
"We left with practical ideas for introducing these technologies into our development workflows responsibly."
Technical Lead
This course is designed for professionals working within software development environments, including:
• Software Developers
• Technical Leads
• Software Architects
• DevOps Engineers
• Engineering Managers
Participants should have experience working within software development or engineering environments.
A general familiarity with modern development tools and workflows will help participants gain the most value from the session.
Understanding how AI technologies can support modern software development practices is becoming an increasingly valuable capability for engineering professionals.
This course is relevant for professionals working in software engineering, technical leadership and modern development environments who want to explore how new technologies can support productivity while maintaining engineering practices
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This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.
