Responsible AI in software developmentRAIISDCYCydrillCY-RAIISD1.0<ul>
<li>Understand various aspects of responsible AI</li><li>How to use generative AI responsibly in software development</li><li>Prompt engineering for optimal outcomes</li><li>How to apply generative AI throughout the SDLC</li></ul><p>General development</p><p>All people involved in using GenAI or developing machine learning</p><h4>A brief history of Artificial Intelligence</h4><ul>
<li>The origins of AI</li><li>Neural networks and “probability engines”</li><li>Early ML coding tools</li><li>The AI coding revolution of the 2020s</li><li>Threats against ML systems</li></ul>
<h4>Responsible AI</h4><ul>
<li>What is responsible AI?</li><li>Accountability and transparency</li><li>Mitigation of harmful bias</li><li>Validity and reliability</li><li>Validity and reliability – non-determinism of the code</li><li>Demonstration – Experimenting with validity and reliability in Copilot</li><li>Explainability and interpretability</li><li>Safety, security, privacy and resilience</li><li>Security and responsible AI in software development</li></ul>
<h4>Using GenAI responsibly in software development</h4><ul>
<li>LLM code generation basics</li><li>Basic building blocks and concepts</li><li>Prompt templating</li><li>System prompts in AI-driven coding</li><li>GenAI tools in coding: Copilot, Codeium and others</li><li>Can AI… boost your productivity?</li><li>Can AI… take care of the ‘boring parts’?</li><li>Can AI… be more thorough?</li><li>Reviewing generated code – the black box blues</li><li>The danger of hallucinations</li><li>Can AI… teach you how to code (better)?</li><li>Demonstration – Experimenting with an unfamiliar API in Copilot</li><li>The effect of GenAI on programming skills</li><li>Some further long-term effects of using GenAI</li><li>Where AI code generation doesn’t do well</li><li>Prompt engineering
<ul>
<li>Why is a good prompt so important?</li><li>Establishing the context for generative AI</li><li>Zero-shot, one-shot, and few-shot prompting</li><li>Reasoning-based prompt engineering, chain-of-thought</li><li>Demonstration – Experimenting with prompts in Copilot</li><li>Enforcing and following token limits</li><li>Prompt patterns
<ul>
<li>Prompt patterns and prompt priming</li><li>The 6 categories of prompt patterns</li><li>Prompt pattern: Meta Language Creation</li><li>Prompt pattern: Persona</li><li>Prompt pattern: Visualization Generator</li><li>Prompt pattern: Fact Check List</li><li>Prompt pattern: Alternative Approaches</li><li>Prompt pattern: Refusal Breaker</li><li>Prompt pattern: Flipped Interaction</li><li>Prompt pattern: Context Manager</li></ul></li><li>Some further prompting approaches
<ul>
<li>Least-to-Most and Self-Planning: decomposition of complex tasks</li><li>Demonstration – Task decomposition with Copilot</li><li>Prompt engineering techniques for refinement and iteration</li><li>Unit tests, TDD and GenAI</li><li>Demonstration – Test-based code generation with Copilot</li></ul></li></ul></li></ul>
<h4>Integrating generative AI into the SDLC</h4><ul>
<li>Using GenAI beyond code generation</li><li>Using AI during requirements specification</li><li>Prompt patterns for requirements capturing</li><li>Software design and AI</li><li>Prompt patterns for software design</li><li>Demonstration – Requirements capturing and API design with Copilot</li><li>Using AI during implementation</li><li>Prompt patterns for implementation</li><li>Demonstration – Finding hidden assumptions with Copilot</li><li>Using AI during testing and QA</li><li>Using AI during maintenance</li><li>Prompt patterns for refactoring</li><li>Demonstration – Experimenting with code refactoring in Copilot</li><li>Prompt patterns for change request simulation</li></ul>
<h4>Security of AI-generated code</h4><ul>
<li>Security of AI generated code</li><li>Practical attacks against code generation tools</li><li>Dependency hallucination via generative AI</li><li>Case study – A history of GitHub Copilot weaknesses (up to mid 2024)</li><li>A sample vulnerability
<ul>
<li>Path traversal</li><li>Demonstration – Path traversal</li><li>Path traversal-related examples</li><li>Path traversal best practices</li><li>Demonstration – Path canonicalization</li><li>Demonstration – Experimenting with path traversal in Copilot</li></ul></li></ul>
<h4>Summary and takeaways</h4><ul>
<li>Responsible AI principles in software development</li><li>Generative AI – Resources and additional guidance</li></ul><ul>
<li>A brief history of Artificial Intelligence</li><li>Responsible AI</li><li>Using GenAI responsibly in software development</li><li>Summary and takeaways</li></ul>- Understand various aspects of responsible AI
- How to use generative AI responsibly in software development
- Prompt engineering for optimal outcomes
- How to apply generative AI throughout the SDLCGeneral developmentAll people involved in using GenAI or developing machine learningA brief history of Artificial Intelligence
- The origins of AI
- Neural networks and “probability engines”
- Early ML coding tools
- The AI coding revolution of the 2020s
- Threats against ML systems
Responsible AI
- What is responsible AI?
- Accountability and transparency
- Mitigation of harmful bias
- Validity and reliability
- Validity and reliability – non-determinism of the code
- Demonstration – Experimenting with validity and reliability in Copilot
- Explainability and interpretability
- Safety, security, privacy and resilience
- Security and responsible AI in software development
Using GenAI responsibly in software development
- LLM code generation basics
- Basic building blocks and concepts
- Prompt templating
- System prompts in AI-driven coding
- GenAI tools in coding: Copilot, Codeium and others
- Can AI… boost your productivity?
- Can AI… take care of the ‘boring parts’?
- Can AI… be more thorough?
- Reviewing generated code – the black box blues
- The danger of hallucinations
- Can AI… teach you how to code (better)?
- Demonstration – Experimenting with an unfamiliar API in Copilot
- The effect of GenAI on programming skills
- Some further long-term effects of using GenAI
- Where AI code generation doesn’t do well
- Prompt engineering
- Why is a good prompt so important?
- Establishing the context for generative AI
- Zero-shot, one-shot, and few-shot prompting
- Reasoning-based prompt engineering, chain-of-thought
- Demonstration – Experimenting with prompts in Copilot
- Enforcing and following token limits
- Prompt patterns
- Prompt patterns and prompt priming
- The 6 categories of prompt patterns
- Prompt pattern: Meta Language Creation
- Prompt pattern: Persona
- Prompt pattern: Visualization Generator
- Prompt pattern: Fact Check List
- Prompt pattern: Alternative Approaches
- Prompt pattern: Refusal Breaker
- Prompt pattern: Flipped Interaction
- Prompt pattern: Context Manager
- Some further prompting approaches
- Least-to-Most and Self-Planning: decomposition of complex tasks
- Demonstration – Task decomposition with Copilot
- Prompt engineering techniques for refinement and iteration
- Unit tests, TDD and GenAI
- Demonstration – Test-based code generation with Copilot
Integrating generative AI into the SDLC
- Using GenAI beyond code generation
- Using AI during requirements specification
- Prompt patterns for requirements capturing
- Software design and AI
- Prompt patterns for software design
- Demonstration – Requirements capturing and API design with Copilot
- Using AI during implementation
- Prompt patterns for implementation
- Demonstration – Finding hidden assumptions with Copilot
- Using AI during testing and QA
- Using AI during maintenance
- Prompt patterns for refactoring
- Demonstration – Experimenting with code refactoring in Copilot
- Prompt patterns for change request simulation
Security of AI-generated code
- Security of AI generated code
- Practical attacks against code generation tools
- Dependency hallucination via generative AI
- Case study – A history of GitHub Copilot weaknesses (up to mid 2024)
- A sample vulnerability
- Path traversal
- Demonstration – Path traversal
- Path traversal-related examples
- Path traversal best practices
- Demonstration – Path canonicalization
- Demonstration – Experimenting with path traversal in Copilot
Summary and takeaways
- Responsible AI principles in software development
- Generative AI – Resources and additional guidance- A brief history of Artificial Intelligence
- Responsible AI
- Using GenAI responsibly in software development
- Summary and takeaways1 day750.00750.00750.00750.00750.00