Episode Details

Daniel has built a prompt enhancement node that takes raw voice-to-text input and decomposes it into questions, context data, and host notes. It works well — but how do you make it reliable when it runs thousands of times? We explore four design principles for building meta-prompts that structure other prompts: classification before extraction, real voice-capture few-shot examples, explicit schemas with fallback fields, and chain-of-thought reasoning for ambiguous cases. If you're building a high-volume AI agent workflow that processes human speech, this episode is about making it never break in ways you don't catch.