The Context Trap: When Detailed Specs Overwhelm AI Assistants

Four AIs, one todo app, and the importance of bounded contexts

I spent yesterday afternoon experimenting with GitHub's Spec Kit¹ and discovered something crucial about how AI coding assistants handle specifications. Four different AIs, same todo app spec, wildly different results. The pattern that emerged challenges how we think about AI-driven development.

My ad-hoc experiment revealed a fundamental constraint in how these models work, and understanding it could define how some teams approach software development in the near future.

The Four-Way AI Experiment

Same seed prompts. Four different AI coding assistants. From the seed prompt Spec Kit generated the actual specification, plan, and tasks. For context, spec-driven development means defining what software should do before writing code, creating a clear contract between intention and implementation.

GitHub Copilot (Claude Sonnet 4): Got 85% complete after 1.5 hours, then hit an auth state issue it couldn't resolve. The conversation history grew so large that it lost track of earlier decisions.

GitHub Copilot (GPT 5): Never made it past setup. Dependency issues compounded, (failed npm run build), as the context filled with error messages and attempted fixes.

Grok code fast 1: The winner (sort of). This appears to be a KISS-tuned model that stripped many details from my spec (goodbye dark mode), which inadvertently created a smaller, manageable scope that gave it a chance to actually complete the implementation.

Claude Code: Tried to honor every requirement, made the architectural decision to separate backend and frontend code, then got lost trying to resolve import path issues between them.

Why Three AIs Failed (and Why One Succeeded)