{"id":7895,"date":"2023-10-30T23:13:23","date_gmt":"2023-10-30T22:13:23","guid":{"rendered":"https:\/\/joapen.com\/blog\/?p=7895"},"modified":"2024-01-18T16:01:48","modified_gmt":"2024-01-18T15:01:48","slug":"guardrails-ai-interview","status":"publish","type":"post","link":"https:\/\/joapen.com\/blog\/2023\/10\/30\/guardrails-ai-interview\/","title":{"rendered":"Guardrails AI, interview"},"content":{"rendered":"\n<p>This is a short summary of Data Driven NYC&#8217;s interview to the co-founder and CEO of Guardrails AI <a href=\"https:\/\/www.linkedin.com\/in\/shreya-rajpal\/\" target=\"_blank\" rel=\"noreferrer noopener\">Shreya Rajpal.<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Guardrails AI offers:<\/h2>\n\n\n\n<p><a href=\"https:\/\/docs.guardrailsai.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Guardrails AI<\/a> is a fully open source library that assures interactions with Large Language Models (LLMs). It offers<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Framework for creating custom validators.<\/li>\n\n\n\n<li>Orchestration of prompting \u2192 verification \u2192 re-prompting.<\/li>\n\n\n\n<li>Library of commonly used validators for multiple use cases.<\/li>\n\n\n\n<li>Specification language for communicating requirements to LLM.<\/li>\n<\/ul>\n\n\n\n<p>The company is being build right now:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Run time guards.<\/li>\n\n\n\n<li>Validators: Independent check you do for any identified risk that you have registered (for instance: a given example of hallucination).<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">A Map<\/h2>\n\n\n\n<p>A Wardley Map to visually illustrate the problem Guardrails AI is trying to solve:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><span style=\"text-decoration: underline;\">Independent validation and verification<\/span><\/strong> is something that will be happening in the near future, not only for compliance reasons but for real time check that enable the Gen-AI solution to do not fall into mistakes or erosion of the brand.<\/li>\n\n\n\n<li>Guardrails AI is being build right now, so by that reason is in red in the map.<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"524\" src=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2023\/10\/image-13-1024x524.png\" alt=\"\" class=\"wp-image-7900\" srcset=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2023\/10\/image-13-1024x524.png 1024w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2023\/10\/image-13-300x154.png 300w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2023\/10\/image-13-768x393.png 768w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2023\/10\/image-13.png 1316w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Retrieval-Augmented Generation (RAG)<\/h2>\n\n\n\n<p>It&#8217;s a technique that combines the abilities of a pre-trained language model with an external<br>knowledge source to enhance its performance, especially in providing up-to-date or very specific information.<\/p>\n\n\n\n<p>A simple explanation:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><span style=\"text-decoration: underline;\"><strong>Retrieval<\/strong><\/span>: When you ask a question, the system first retrieves relevant information from a large database of text. It&#8217;s like looking up reference material to find the best possible answers.<\/li>\n\n\n\n<li><span style=\"text-decoration: underline;\"><strong>Augmented<\/strong><\/span>: The information retrieved is then combined with the knowledge already present in the language model. This enhances the model&#8217;s ability to generate a response.<\/li>\n\n\n\n<li><span style=\"text-decoration: underline;\"><strong>Generation<\/strong><\/span>: Finally, the system generates a response using both its pre-trained knowledge and the additional information it just retrieved.<\/li>\n<\/ul>\n\n\n\n<p>The advantage of RAG is that it allows the model to provide more accurate and up-to-date responses than it could with just its pre-trained knowledge.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"950\" height=\"474\" src=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2023\/10\/image-12.png\" alt=\"\" class=\"wp-image-7897\" srcset=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2023\/10\/image-12.png 950w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2023\/10\/image-12-300x150.png 300w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2023\/10\/image-12-768x383.png 768w\" sizes=\"auto, (max-width: 950px) 100vw, 950px\" \/><figcaption class=\"wp-element-caption\">source:\u00a0<a href=\"https:\/\/www.ml6.eu\/blogpost\/leveraging-llms-on-your-domain-specific-knowledge-base\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.ml6.eu\/blogpost\/leveraging-llms-on-your-domain-specific-knowledge-base<\/a><\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Update January 2024, Amazon RefChecker<\/h2>\n\n\n\n<p>Amazon has published this GitHub\u00a0<a href=\"https:\/\/github.com\/amazon-science\/RefChecker\" target=\"_blank\" rel=\"noreferrer noopener\">repo<\/a>, with functionality to increase the quality of responses given by LLMs. The initial scope is more limited than Guardrails AI, where here the focus is the references check.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.amazon.science\/blog\/new-tool-dataset-help-detect-hallucinations-in-large-language-models\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.amazon.science\/blog\/new-tool-dataset-help-detect-hallucinations-in-large-language-models<\/a><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"696\" src=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2024\/01\/image-18-1024x696.png\" alt=\"Possible relationships between an LLM\u2019s response to a prompt and the corresponding references.\" class=\"wp-image-8140\" srcset=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2024\/01\/image-18-1024x696.png 1024w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2024\/01\/image-18-300x204.png 300w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2024\/01\/image-18-768x522.png 768w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2024\/01\/image-18.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Possible relationships between an LLM\u2019s response to a prompt and the corresponding references.<\/figcaption><\/figure>\n\n\n\n<p>As usual, any constructive feedback is welcome.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This is a short summary of Data Driven NYC&#8217;s interview to the co-founder and CEO of Guardrails AI Shreya Rajpal. Guardrails AI offers: Guardrails AI is a fully open source library that assures interactions with Large Language Models (LLMs). It offers The company is being build right now: A Map A Wardley Map to visually &#8230; <a title=\"Guardrails AI, interview\" class=\"read-more\" href=\"https:\/\/joapen.com\/blog\/2023\/10\/30\/guardrails-ai-interview\/\" aria-label=\"Read more about Guardrails AI, interview\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[268],"tags":[],"class_list":["post-7895","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Guardrails AI, interview -<\/title>\n<meta name=\"description\" content=\"This is a short summary of Data Driven NYC&#039;s interview to the co-founder and CEO of Guardrails AI Shreya Rajpal. 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