Cave Prompt: An Experiment in Semantic Prompt Compilation
Large context windows are great, but they don't solve a common problem:
Important requirements often get buried inside long prompts and conversations.
In many cases, the model isn't failing because it's incapable. It's failing because the signal-to-noise ratio of the prompt is poor.
So I built Cave Prompt, a small experiment that treats prompts a bit more like source code.
Instead of sending raw user input directly to an LLM, Cave Prompt:
- Extracts intent
- Identifies constraints
- Removes low-information noise
- Builds a structured semantic representation (IR)
- Generates an optimized execution prompt
The goal isn't to replace prompt engineering, but to make prompts more consistent and easier for models to reason about.
I'm still experimenting with the approach and would love feedback from others building AI agents, coding assistants, or LLM workflows.
Top comments (1)
The core problem you're attacking is real: most "the AI did the wrong thing" cases are actually "the AI understood the wrong thing," ambiguous requirements in, confident wrong output out. Forcing the requirements into a clearer structure before generation is higher-leverage than any amount of post-hoc prompting. The thing I'd add: have the model restate its understanding and the assumptions it's making before it acts, so you catch the misread early instead of after it's built the wrong thing. I lean on that assumption-surfacing step in Moonshift. Does Cave Prompt make the model echo back its interpretation, or structure the input side only?