About the Seminar
In an era where AI generates code faster than engineers can read it, the fundamental nature of software development has shifted.
This course addresses the critical knowledge gap between AI-driven code generation and production-grade engineering.
Participants will learn how to maintain architectural integrity, navigate complex concurrency issues, critically evaluate machine-generated code, and build robust safety nets that enable fast yet secure AI-augmented development.
Who Is This Seminar For?
Software Developers (all levels), Senior Engineers, Backend Developers, Tech Leads, Software Architects, and R&D Managers.
Key Topics
Session 1: From Writing to Judging: Software Engineering in the Age of AI Code Execution
- Topics Covered:
- The New Bottleneck: Shifting focus from code generation to critical code evaluation.
- The "Confidence Trap": Identifying syntactically correct, confidently presented code that is fundamentally flawed.
- Critical Code Review for Machine Output: Developing essential human skills that increase in value alongside AI adoption.
- Practical Failure Modes: Analyzing classic AI edge-case failures, including empty collections, variable overflows, and division-by-zero errors.
Session 2: Concurrency, Algorithms & Architecture: Where AI Fails (and You Need to Rescue It)
- Topics Covered:
- Engineering Intuition Gaps: Why AI defaults to obvious rather than optimal data structures (e.g., accidental complexity).
- The Concurrency Trap: Understanding why AI frequently introduces race conditions, deadlocks, and thread-safety bugs that pass basic tests.
- Performance & Scalability Risks: Identifying hidden architectural flaws in AI-generated backend logic.
- Practical Refactoring Demo: Auditing AI-generated architectural code, uncovering silent concurrency issues, and implementing proper fixes.
Session 3: The AI Leash: Building an Enterprise Safety Net and Rethinking Development Processes
- Topics Covered:
- Tests as the Leash: Designing robust test suites to allow aggressive AI code generation without risking stability.
- Behavior vs. Implementation: Writing tests focused on functional outcomes and leveraging Characterization Tests to lock legacy code before AI refactoring.
- Evolving Agile Processes: Adapting traditional Agile workflows (Sprint Planning, Story Points) for an environment where initial drafts take minutes instead of weeks.
- Modern AI Workflow Lifecycle: Executing fast, secure development loops through Specify Verify Integrate Integrate.