Why do AI coding assistants sometimes deliver impressive results, only to produce disappointing code shortly after? In this session, Gregor Biswanger explains why generative AI in real-world software projects can quickly lead to code duplication, forgotten architectural decisions, context drift, and inconsistent implementations when clear guardrails are missing.

The focus is on Spec-Driven Development: an approach where specifications, rules, and project conventions become the long-term single source of truth for the AI. Instead of simply writing better prompts, you create a stable framework that continuously aligns AI assistants with your architecture, standards, and desired behavior.

Using GitHub Copilot, Gregor demonstrates how this approach works in practice and why the principle is relevant regardless of the specific tool. If you want to use AI productively in real projects without letting your codebase drift out of control, this session provides practical methods for more consistent results, less rework, and better technical decisions.

In this session, Gregor Biswanger shows how Spec-Driven Development makes AI coding assistants more reliable. Instead of simply improving prompts, specifications, rules, and project conventions serve as a long-term single source of truth. The result: more consistent implementations, less context drift, and cleaner codebases in real-world software projects.