{"version":"1.0","provider_name":"Microsoft Research","provider_url":"https:\/\/www.microsoft.com\/en-us\/research","author_name":"Amanda Melfi","author_url":"https:\/\/www.microsoft.com\/en-us\/research\/people\/live-comamanda2aconsulting\/","title":"Prompts As Programs: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization - Microsoft Research","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"iuSwFXcEez\"><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/prompts-as-programs-a-structure-aware-approach-to-efficient-compile-time-prompt-optimization\/\">Prompts As Programs: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/prompts-as-programs-a-structure-aware-approach-to-efficient-compile-time-prompt-optimization\/embed\/#?secret=iuSwFXcEez\" width=\"600\" height=\"338\" title=\"&#8220;Prompts As Programs: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization&#8221; &#8212; Microsoft Research\" data-secret=\"iuSwFXcEez\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/* <![CDATA[ *\/\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/* ]]> *\/\n<\/script>\n","description":"Large language models (LLMs) can now handle longer and more complex inputs, which facilitate the use of more elaborate prompts. However, prompts often require some tuning to improve performance for deployment. Recent work has proposed automatic prompt optimization methods, but as prompt complexity and LLM strength increase, many prompt optimization techniques are no longer sufficient [&hellip;]"}