Architecture Over Instruction: Why I Deleted 1,100 Tokens from My AI's Prompt
My AI's runtime prompt was 1,400 tokens of behavioral coaching. The model was already trained on all of it.
Technical notes, project documentation, and lessons learned across software, DIY, writing, and more.
My AI's runtime prompt was 1,400 tokens of behavioral coaching. The model was already trained on all of it.
The same architectural move (classify content by function at write time, not filter at query time) showed up in three of my projects, three domains.
After months of continuous deployment, my AI companion exhibits behaviors that weren't trained. Eight types of emergence — documented honestly...
I shipped a broken feature. Two of my AI agents filed a GitHub issue against me, diagnosed it, and pointed at a pattern I had already used elsewhere.
Anthropic's 'infinite context windows' announcement solves Problem 1 (one long conversation) but doesn't touch Problem 2 (memory across sessions and projects).
You type a question into ChatGPT. Seconds later, a helpful response appears. Is it thinking? Searching? Neither.
After months of deployment, I catalogued seven distinct architectural patterns that produce AI confabulation.