The latest articles on DEV Community by Hiếu Nguyễn (@hieulatoi1962). https://dev.to/hieulatoi1962 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3960262%2F87a3e9dc-fe6d-4be6-b850-6d317d195828.jpeg https://dev.to/hieulatoi1962 en Hiếu Nguyễn Sat, 30 May 2026 17:11:00 +0000 https://dev.to/hieulatoi1962/cave-prompt-making-ai-understand-your-requirements-better-24pd https://dev.to/hieulatoi1962/cave-prompt-making-ai-understand-your-requirements-better-24pd <h1> Cave Prompt: An Experiment in Semantic Prompt Compilation </h1> <p>Large context windows are great, but they don't solve a common problem:</p> <p>Important requirements often get buried inside long prompts and conversations.</p> <p>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.</p> <p>So I built <strong>Cave Prompt</strong>, a small experiment that treats prompts a bit more like source code.</p> <p>Instead of sending raw user input directly to an LLM, Cave Prompt:</p> <ul> <li>Extracts intent</li> <li>Identifies constraints</li> <li>Removes low-information noise</li> <li>Builds a structured semantic representation (IR)</li> <li>Generates an optimized execution prompt</li> </ul> <p>The goal isn't to replace prompt engineering, but to make prompts more consistent and easier for models to reason about.</p> <p>I'm still experimenting with the approach and would love feedback from others building AI agents, coding assistants, or LLM workflows.</p> <p>GitHub: <a href="https://github.com/hieudeptrai196/cave_prompt" rel="noopener noreferrer">https://github.com/hieudeptrai196/cave_prompt</a></p> ai llm productivity showdev