AI Foundations for Software Developers Training

AI Foundations for Software Developers

• Why AI Changes Software Development
  • From autocomplete to probabilistic code generation
  • How AI differs from compilers, linters, and IDE tools
  • Why AI-generated code “looks right” even when it’s wrong
  • Shifts in responsibility and accountability
• How AI Generates Code
  • High-level overview of large language models
  • Tokens, context windows, and pattern completion
  • Why AI does not “understand” code
  • What training data means for code generation
• Strengths of AI-Assisted Coding
  • Accelerating routine and boilerplate code
  • Exploring unfamiliar APIs and libraries
  • Generating examples and scaffolding
  • Supporting learning and experimentation
• Failure Modes in AI-Generated Code
  • Logical errors and incorrect assumptions
  • Edge cases and missing constraints
  • Outdated or deprecated APIs
  • Overconfidence and fabricated explanations
• Reading and Reviewing AI-Generated Code
  • Treating AI output as a draft, not an answer
  • Techniques for validating correctness
  • Recognizing “convincing nonsense”
  • Knowing when to rewrite instead of patch
• AI and Software Quality
  • Impact on readability and maintainability
  • Consistency with existing codebases
  • Avoiding accidental architectural drift
  • Long-term cost of unchecked AI usage
• Security and Risk Considerations
  • Common insecure patterns introduced by AI
  • Dependency and licensing risks
  • Data leakage and prompt misuse
  • Why AI-generated code must be reviewed for security
• Professional Responsibility and Ethics
  • Accountability for AI-assisted code
  • Attribution and intellectual property considerations
  • Using AI responsibly in team environments
  • Setting personal and team boundaries
• When Not to Use AI
  • High-risk or safety-critical code
  • Performance-sensitive systems
  • Security and cryptography
  • Regulatory and compliance constraints
• Preparing for AI-Augmented Development
  • Developing healthy skepticism and discipline
  • Integrating AI into professional workflows
  • Preparing for tool-specific training (e.g., Copilot)
  • Next steps in AI-augmented software development

Before Taking this Class

Professional experience in software development and familiarity with modern development workflows. No prior AI or machine learning experience is required.

Software/Setup For this Class

No AI tools, programming environments, or software installations are required for this course, though participants should be comfortable reading and reasoning about code.