https://aibussin.com/tags/dimensional-scoring/Recent content in Dimensional Scoring on AIBussin — AI applications, systems and booksHugoen-USThu, 26 Jun 2025 13:31:41 +0100https://aibussin.com/post/compiler/Thu, 26 Jun 2025 13:31:41 +0100https://aibussin.com/post/compiler/<p><strong>How to design a pipeline that turns vague goals into smart prompts</strong></p> <h2 id="-summary">🧪 Summary</h2> <p>Why spend hours engineering prompts when AI can optimize its own instructions. This blog post introduces a novel approach toward creating a self-improving AI by treating prompts as programs. Traditional AI systems often rely on static instructions rigid and limited in adaptability. Here, we present a different perspective: viewing the Large Language Model (LLM) as a <strong>prompt compiler</strong> capable of dynamically transforming raw instructions into optimized prompts through iterative cycles of decomposition, evaluation, and intelligent reassembly.</p>https://aibussin.com/post/thoughts/Mon, 23 Jun 2025 11:10:59 +0100https://aibussin.com/post/thoughts/<blockquote> <p>How a self-evolving AI learns to reflect, score, and rewrite its own reasoning</p> </blockquote> <h2 id="-summary">🧪 Summary</h2> <p>What if an AI could think not just solve problems, but reevaluate its beliefs in the face of new information?</p> <p>In this post, we introduce a system that does exactly that. At the core of our pipeline is a lightweight scoring model called MR.Q, responsible for evaluating ideas and choosing the best ones. But when it encounters a new domain, a new goal, or a shift in task format, it doesn’t freeze it adapts.</p>