I thought that scaling an AI system simply meant refining a single, massive prompt. I wrote complex system instructions, packed the context window with rules, and expected a single stateless API call to act as a researcher, analyst, and copywriter all at once.
I thought that scaling an AI system simply meant refining a single, massive prompt.
When processing dynamic data streams, the model flattened nuanced details, skipped critical execution steps, and regularly generated highly confident hallucinations.
I wrote complex system instructions, packed the context window with rules, and expected a single stateless API call to act as a researcher, analyst, and copywriter all at once.
The page is ready to read now. The fuller skim-friendly version will appear here automatically.
I thought that scaling an AI system simply meant refining a single, massive prompt. I wrote complex system instructions, packed the context window with rules, and expected a single stateless API call to act as a researcher, analyst, and copywriter all at once. When processing dynamic data streams, the model flattened nuanced details, skipped critical execution steps, and regularly generated highly confident hallucinations.
Models (LLMs), I fell into a trap that almost every developer encounters. In production, this monolithic approach failed repeatedly.
Open the app view to save this story, compare related coverage, and continue from the same source.