<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>Experiment &#8211; Noise</title>
	<atom:link href="https://noise.getoto.net/tag/experiment/feed/" rel="self" type="application/rss+xml" />
	<link>https://noise.getoto.net</link>
	<description>The collective thoughts of the interwebz</description>
	<lastBuildDate>Thu, 08 May 2025 00:00:10 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	<generator>https://wordpress.org/?v=6.8.2</generator>
	<item>
		<title>Streamlining RiskOps with the SOP agent framework</title>
		<link>https://noise.getoto.net/2025/05/08/streamlining-riskops-with-the-sop-agent-framework/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Thu, 08 May 2025 00:00:10 +0000</pubDate>
				<category><![CDATA[Data Analytics]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Experiment]]></category>
		<category><![CDATA[generative AI]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[machine learning]]></category>
		<guid isPermaLink="false">https://engineering.grab.com/streamlining-riskops-with-sop</guid>

					<description><![CDATA[Introduction

In the blog our previous introduction to the SOP-driven LLM Agent Framework, we the potential of LLM agent framework to revolutionise business operations was discussed. Now, we’re excited to explore a compelling use case: automating Accou...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Introducing the SOP-driven LLM agent frameworks</title>
		<link>https://noise.getoto.net/2025/04/25/introducing-the-sop-driven-llm-agent-frameworks/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Fri, 25 Apr 2025 00:00:10 +0000</pubDate>
				<category><![CDATA[Data Analytics]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Experiment]]></category>
		<category><![CDATA[generative AI]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[machine learning]]></category>
		<guid isPermaLink="false">https://engineering.grab.com/introducing-the-sop-drive-llm-agent-framework</guid>

					<description><![CDATA[Introduction

We’re excited to introduce an innovative Large Language Model (LLM) agent framework that reimagines how enterprises can harness the power of AI to streamline operations and boost productivity. At its core, this framework leverages Standar...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>LLM-assisted vector similarity search</title>
		<link>https://noise.getoto.net/2024/10/23/llm-assisted-vector-similarity-search/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Wed, 23 Oct 2024 00:00:10 +0000</pubDate>
				<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Experiment]]></category>
		<category><![CDATA[generative AI]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[machine learning]]></category>
		<guid isPermaLink="false">https://engineering.grab.com/llm-assisted-vector-similarity-search</guid>

					<description><![CDATA[Introduction

As the complexity of data retrieval requirements continue to grow, traditional search methods often struggle to provide relevant and accurate results, especially for nuanced or conceptual queries. Vector similarity search has emerged as a...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Leveraging RAG-powered LLMs for Analytical Tasks</title>
		<link>https://noise.getoto.net/2024/10/09/leveraging-rag-powered-llms-for-analytical-tasks/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Wed, 09 Oct 2024 00:00:10 +0000</pubDate>
				<category><![CDATA[design]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Experiment]]></category>
		<category><![CDATA[generative AI]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[machine learning]]></category>
		<guid isPermaLink="false">https://engineering.grab.com/transforming-the-analytics-landscape-with-RAG-powered-LM</guid>

					<description><![CDATA[Introduction

Retrieval-Augmented Generation (RAG) is a powerful process that is designed to integrate direct function calling to answer queries more efficiently by retrieving relevant information from a broad database. In the rapidly evolving business...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Leveraging RAG-powered LLMs for Analytical Tasks</title>
		<link>https://noise.getoto.net/2024/10/09/leveraging-rag-powered-llms-for-analytical-tasks-2/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Wed, 09 Oct 2024 00:00:10 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Experiment]]></category>
		<category><![CDATA[generative AI]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[machine learning]]></category>
		<guid isPermaLink="false">https://engineering.grab.com/transforming-the-analytics-landscape-with-RAG-powered-LLM</guid>

					<description><![CDATA[Introduction

Retrieval-Augmented Generation (RAG) is a powerful process that is designed to integrate direct function calling to answer queries more efficiently by retrieving relevant information from a broad database. In the rapidly evolving business...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Unveiling the process: The creation of our powerful campaign builder</title>
		<link>https://noise.getoto.net/2024/09/10/unveiling-the-process-the-creation-of-our-powerful-campaign-builder/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Tue, 10 Sep 2024 00:00:10 +0000</pubDate>
				<category><![CDATA[design]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Experiment]]></category>
		<category><![CDATA[generative AI]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[machine learning]]></category>
		<guid isPermaLink="false">https://engineering.grab.com/the-creation-of-our-powerful-campaign-builder</guid>

					<description><![CDATA[In a previous blog, we introduced Trident, Grab’s internal marketing campaign platform. Trident empowers our marketing team to configure If This, Then That (IFTTT) logic and processes real-time events based on that.

While we mainly covered how we scal...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Chimera Sandbox: A scalable experimentation and development platform for Notebook services</title>
		<link>https://noise.getoto.net/2024/08/27/chimera-sandbox-a-scalable-experimentation-and-development-platform-for-notebook-services/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Tue, 27 Aug 2024 00:00:10 +0000</pubDate>
				<category><![CDATA[design]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Experiment]]></category>
		<category><![CDATA[generative AI]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[machine learning]]></category>
		<guid isPermaLink="false">https://engineering.grab.com/chimera-sandbox</guid>

					<description><![CDATA[Key to innovation and improvement in machine learning (ML) models is the ability for rapid iteration. Our team, Chimera, part of the Artificial Intelligence (AI) Platform team, provides the essential compute infrastructure, ML pipeline components, and ...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Grab Experiment Decision Engine &#8211; a Unified Toolkit for Experimentation</title>
		<link>https://noise.getoto.net/2024/04/09/grab-experiment-decision-engine-a-unified-toolkit-for-experimentation/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Tue, 09 Apr 2024 02:22:10 +0000</pubDate>
				<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Econometrics]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Experiment]]></category>
		<category><![CDATA[Python Package]]></category>
		<category><![CDATA[Statistics]]></category>
		<guid isPermaLink="false">https://engineering.grab.com/grabx-decision-engine</guid>

					<description><![CDATA[Introduction

This article introduces the GrabX Decision Engine, an internal open-source package that offers a comprehensive framework for designing and analysing experiments conducted on online experiment platforms. The package encompasses a wide rang...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Automated Experiment Analysis &#8211; Making experimental analysis scalable</title>
		<link>https://noise.getoto.net/2022/05/30/automated-experiment-analysis-making-experimental-analysis-scalable/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Mon, 30 May 2022 00:20:55 +0000</pubDate>
				<category><![CDATA[Azure Databricks]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Experiment]]></category>
		<category><![CDATA[Experimental analysis]]></category>
		<guid isPermaLink="false">https://engineering.grab.com/automated-experiment-analysis</guid>

					<description><![CDATA[Introduction

Trustworthy experiments are key to making sound decisions, so analysts and data scientists put a lot of effort into analysing them and making business impacts. An extension of Grab’s Experimentation (GrabX) platform, Automated Experiment ...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
	</channel>
</rss>

<!--
Performance optimized by W3 Total Cache. Learn more: https://www.boldgrid.com/w3-total-cache/

Object Caching 30/149 objects using Memcached
Page Caching using Disk: Enhanced 
Lazy Loading (feed)
Database Caching using Memcached

Served from: noise.getoto.net @ 2025-12-11 15:21:57 by W3 Total Cache
-->