Prompt Engineering Alone Isn’t Enough — We Need Prompt Understanding
This was a thought-provoking read, Jakob.
I’ve often focused on crafting better prompts — only to realize that even well-structured instructions can lead to wildly misaligned outputs. Your framing of “prompt understanding” as the missing half of the equation really resonates.
The idea of being able to debug how an LLM interprets a prompt — like stepping into its cognitive process — could be a game-changer, especially for safety-critical or user-facing applications.
Thanks for surfacing this gap. Looking forward to more on how we might build those interpretability tools.
Loved the depth here. Aided prompt understanding feels like the missing layer between user intent and AI results. Glad to see it framed as a UX challenge, not just a technical one.
Prompt Engineering Alone Isn’t Enough — We Need Prompt Understanding
This was a thought-provoking read, Jakob.
I’ve often focused on crafting better prompts — only to realize that even well-structured instructions can lead to wildly misaligned outputs. Your framing of “prompt understanding” as the missing half of the equation really resonates.
The idea of being able to debug how an LLM interprets a prompt — like stepping into its cognitive process — could be a game-changer, especially for safety-critical or user-facing applications.
Thanks for surfacing this gap. Looking forward to more on how we might build those interpretability tools.
Loved the depth here. Aided prompt understanding feels like the missing layer between user intent and AI results. Glad to see it framed as a UX challenge, not just a technical one.