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index.html
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<!DOCTYPE html>
<html lang="en">
<head>
<link rel="stylesheet" href="css/v1.css" />
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Free my People</title>
</head>
<body>
<header>
<img src="assets/assets/stickk-removebg-preview.png" alt="" id="icon" />
<ul id="ul1">
<li class="hbar"><a href="cj.html">H</a></li>
<li class="hbar"><a href="v0.html">O</a></li>
<li class="hbar"><a href="cj.html">M</a></li>
<li class="hbar"><a href="v0.html">E</a></li>
</ul>
<form action="" method="">
<label for="search"></label
><input type="text" name="search" id="search" placeholder="search" />
<a href="cj.html"
><input style="margin-right: 25px" type="button" value="search"
/></a>
</form>
</header>
<main>
<img src="assets/assets/stickk-removebg-preview.png" id="iconfixed">
<div id="bigbox"><div class="box topp">
<h2 >Skills</h2>
<ul class="list">
<li class="points">Problem solving</li>
<li class="points">webscraping</li>
<li class="points">automation</li>
<a href="v0.html"><li class="points">HTML/CSS</li></a>
<li></li>
</ul>
</div>
<div class="box">
<h2>Languages and Libraries</h2>
<ul class="list">
<li class="points">Arabic</li>
<li class="points">English</li>
<li class="points">Python</li>
</ul>
</div>
<div class="box topp">
<h2>Currently Learning</h2>
<ul class="list">
<li class="points">Machine Learning</li>
<li class="points">C and C++</li>
<li></li>
</ul>
</div><div class="box topp">
<h2 >Skills</h2>
<ul class="list">
<li class="points">Problem solving</li>
<li class="points">webscraping</li>
<li class="points">automation</li>
<a href="v0.html"><li class="points">HTML/CSS</li></a>
<li></li>
</ul>
</div>
<div class="box">
<h2>Languages and Libraries</h2>
<ul class="list">
<li class="points">Arabic</li>
<li class="points">English</li>
<li class="points">Python</li>
</ul>
</div>
<div class="box topp">
<h2>Currently Learning</h2>
<ul class="list">
<li class="points">Machine Learning</li>
<li class="points">C and C++</li>
<li></li>
</ul>
</div></div>
<section><article>RAG is a two-step process involving retrieval and generation. In the retrieval phase, when the user submits a query, this triggers a relevancy search among the external documents. The RAG system then grabs snippets of information that are related to the query and adds them to the prompt in the context window. The prompt and the additional information are fed into the generative AI model, which formulates a response based on its inbuilt knowledge plus the additional information from the RAG search.</article>
<article class="special topp">The external documents are typically housed in a vector database, which uses an embedding model to convert data in any form (text, images, audio, video) into groups of numbers, or vectors, based on their meaning. When the user writes a prompt, this is similarly converted into numerical representations, or vectors, to make it compatible with the database entries.</article>
<article>Next, the RAG system performs a nearest-neighbor search to identify database items that are most similar in meaning to the user’s query. (This is a notably different type of matching than that of foundation models. Generative AI models formulate responses by matching patterns or words, while RAG systems retrieve data based on similarity of meaning or semantic searches. Consequently, these responses tend to be more relevant and accurate.) Finally, the retrieved information is attached to the user’s prompt via the context window and used to craft a superior response.</article></section>
<img src="assets/images.jpg" class="topp" id="mrobot" style="margin: 20px" />
<p style="font-size: 50px;">For a RAG framework to provide comprehensive, accurate responses, the model training must be similarly comprehensive and precise. While AI can facilitate and automate some parts of the training process, the fundamental training tasks for RAG frameworks require qualified human domain-expert annotators. <br>
To train and integrate a RAG framework, domain experts must guide the model through multiple stages of search queries and responses by providing high-quality examples. <br> Training tasks typically include parsing user prompts, formulating search queries to search external sources, assessing retrieved data for relevance and completeness, and composing accurate, well-written, ethical responses. As always, the higher the quality of model trainers, training methods and data sources, the better the model’s performance.</p>
</main>
<footer>
<marquee
class="ticker"
behavior="scroll"
direction="left"
scrollamount="15"
>
<ul id="ul2">
<img src="assets/stickk-removebg-preview.png" class="img2" />
<li class="ffree">Free Palestine</li>
<img src="assets/stickk-removebg-preview.png" class="img2" />
<li class="ffree">Free Palestine</li>
<img src="assets/stickk-removebg-preview.png" class="img2" />
<li class="ffree">Free Palestine</li>
<img src="assets/stickk-removebg-preview.png" class="img2" />
<li class="ffree">Free Palestine</li>
<img src="assets/stickk-removebg-preview.png" class="img2" />
<li class="ffree">Free Palestine</li>
<img src="assets/stickk-removebg-preview.png" class="img2" />
<li class="ffree">Free Palestine</li>
<img src="assets/stickk-removebg-preview.png" class="img2" />
<li class="ffree">Free Palestine</li>
<img src="assets/stickk-removebg-preview.png" class="img2" />
<li class="ffree">Free Palestine</li>
<img src="assets/stickk-removebg-preview.png" class="img2" />
<li class="ffree">Free Palestine</li>
<img src="assets/stickk-removebg-preview.png" class="img2" />
<li class="ffree">Free Palestine</li>
<img src="assets/stickk-removebg-preview.png" class="img2" />
<li class="ffree">Free Palestine</li>
<img src="assets/stickk-removebg-preview.png" class="img2" />
<li class="ffree">Free Palestine</li>
<img src="assets/stickk-removebg-preview.png" class="img2" />
<li class="ffree">Free Palestine</li>
<img src="assets/stickk-removebg-preview.png" class="img2" />
</ul>
</marquee>
</footer>
</body>
</html>