{"version":"1.0","provider_name":"insideBIGDATA","provider_url":"https:\/\/insidebigdata.com","author_name":"Editorial Team","author_url":"https:\/\/insidebigdata.com\/author\/editorial\/","title":"Crafting Precision Content Using Large Language Models\u00a0 - insideBIGDATA","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"qVYh4cTITz\"><a href=\"https:\/\/insidebigdata.com\/2023\/12\/11\/crafting-precision-content-using-large-language-models\/\">Crafting Precision Content Using Large Language Models\u00a0<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/insidebigdata.com\/2023\/12\/11\/crafting-precision-content-using-large-language-models\/embed\/#?secret=qVYh4cTITz\" width=\"600\" height=\"338\" title=\"&#8220;Crafting Precision Content Using Large Language Models\u00a0&#8221; &#8212; insideBIGDATA\" data-secret=\"qVYh4cTITz\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/*! This file is auto-generated *\/\n!function(c,d){\"use strict\";var e=!1,o=!1;if(d.querySelector)if(c.addEventListener)e=!0;if(c.wp=c.wp||{},c.wp.receiveEmbedMessage);else if(c.wp.receiveEmbedMessage=function(e){var t=e.data;if(!t);else if(!(t.secret||t.message||t.value));else if(\/[^a-zA-Z0-9]\/.test(t.secret));else{for(var r,s,a,i=d.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),n=d.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),o=new RegExp(\"^https?:$\",\"i\"),l=0;l<n.length;l++)n[l].style.display=\"none\";for(l=0;l<i.length;l++)if(r=i[l],e.source!==r.contentWindow);else{if(r.removeAttribute(\"style\"),\"height\"===t.message){if(1e3<(s=parseInt(t.value,10)))s=1e3;else if(~~s<200)s=200;r.height=s}if(\"link\"===t.message)if(s=d.createElement(\"a\"),a=d.createElement(\"a\"),s.href=r.getAttribute(\"src\"),a.href=t.value,!o.test(a.protocol));else if(a.host===s.host)if(d.activeElement===r)c.top.location.href=t.value}}},e)c.addEventListener(\"message\",c.wp.receiveEmbedMessage,!1),d.addEventListener(\"DOMContentLoaded\",t,!1),c.addEventListener(\"load\",t,!1);function t(){if(o);else{o=!0;for(var e,t,r,s=-1!==navigator.appVersion.indexOf(\"MSIE 10\"),a=!!navigator.userAgent.match(\/Trident.*rv:11\\.\/),i=d.querySelectorAll(\"iframe.wp-embedded-content\"),n=0;n<i.length;n++){if(!(r=(t=i[n]).getAttribute(\"data-secret\")))r=Math.random().toString(36).substr(2,10),t.src+=\"#?secret=\"+r,t.setAttribute(\"data-secret\",r);if(s||a)(e=t.cloneNode(!0)).removeAttribute(\"security\"),t.parentNode.replaceChild(e,t);t.contentWindow.postMessage({message:\"ready\",secret:r},\"*\")}}}}(window,document);\n<\/script>\n","thumbnail_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/06\/GenerativeAI_shutterstock_2284999159_special.jpg","thumbnail_width":1100,"thumbnail_height":550,"description":"[SPONSORED POST] Our friends over at DAC recently explored the potential of LLMs to interpret or generate text considering numerous parameters with extraordinary precision. They have demonstrated their ability to evaluate unstructured text blocks and grade them along various scales to great success. Additionally, they have been able to generate specific, tailored content using a complex series of instructions that can be tuned with precision to the same set of scales.\u00a0"}