Exactly. What you describe as 'parsing work' is, at the architectural level, a high-entropy search across the attention heads. When a prompt is a 'wall of text,' the model's routing logic has to maintain multiple competing states, which physically manifests as jitter and increased power draw per token.
By using semantic blocks (like in your flompt framework), you are essentially performing Inference Pre-conditioning. You’re forcing the model into a narrow, low-entropy path from the very first token.
This is why we focus on SDAG [https://github.com/alexbuiko-sketch/SDAG-Standard] — to provide a metric for this 'routing efficiency.' In the future, we might even be able to use SDAG signals to 'score' prompt architectures like flompt based on how much hardware-level stress they reduce. Structural clarity isn't just a convenience for the model; it's a physical optimization of the compute cycle.
By using semantic blocks (like in your flompt framework), you are essentially performing Inference Pre-conditioning. You’re forcing the model into a narrow, low-entropy path from the very first token.
This is why we focus on SDAG [https://github.com/alexbuiko-sketch/SDAG-Standard] — to provide a metric for this 'routing efficiency.' In the future, we might even be able to use SDAG signals to 'score' prompt architectures like flompt based on how much hardware-level stress they reduce. Structural clarity isn't just a convenience for the model; it's a physical optimization of the compute cycle.