We ran 1400 RAG experiments so you don't have to! Read our blog (👇) to find out how you can improve your RAG pipeline. https://lnkd.in/dBCQnVTv #RAG #llm #ai #llamaindex
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🚀 Integrating knowledge with Large Language Models (LLMs) via a Retrieval-Augmented Generation (RAG) system is an impressive feat, but ensuring it consistently delivers top-notch answers while seamlessly integrating new documents and features? Now, that's a whole different ball game – and a challenging one at that! 🔍 Thankfully, the innovative 'ragas' framework is changing the game, making these daunting tasks achievable and significantly boosting the performance of RAG and AI systems. 📖 Dive deeper into how we're tackling these challenges head-on and enhancing our AI's capabilities in Pondhouse Data OG's latest blog. Your journey to AI excellence starts here: Pondhouse Data OG Let's navigate this complex yet exciting AI landscape together! 💡🤖
📖 New Guide: How to ensure RAG quality 📖 At Pondhouse Data OG, we are committed to building the highest quality AI document systems for technical documents. RAG is revolutionizing how AI models integrate external knowledge, producing more accurate and contextually rich responses. Yet, the journey to optimizing these pipelines is filled with challenges. That's where ragas comes into play. Providing a structured framework for evaluating RAG systems, ragas empowers researchers and developers to fine-tune their implementations, pushing the boundaries of AI capabilities. Our next blog explores the mechanics of RAG and the pivotal role of ragas in their enhancement but also offers a comprehensive guide on setting up and leveraging ragas for significant improvements. With modules like TestsetGenerator and evaluate, ragas is poised to ensure the relevance, accuracy, and efficiency of RAG pipelines, marking a milestone in technical document processing. https://lnkd.in/dfv7v76Z
Improving Retrieval Augmented Generation: A Step-by-Step Evaluation of RAG Pipelines
pondhouse-data.com
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📖 New Guide: How to ensure RAG quality 📖 At Pondhouse Data OG, we are committed to building the highest quality AI document systems for technical documents. RAG is revolutionizing how AI models integrate external knowledge, producing more accurate and contextually rich responses. Yet, the journey to optimizing these pipelines is filled with challenges. That's where ragas comes into play. Providing a structured framework for evaluating RAG systems, ragas empowers researchers and developers to fine-tune their implementations, pushing the boundaries of AI capabilities. Our next blog explores the mechanics of RAG and the pivotal role of ragas in their enhancement but also offers a comprehensive guide on setting up and leveraging ragas for significant improvements. With modules like TestsetGenerator and evaluate, ragas is poised to ensure the relevance, accuracy, and efficiency of RAG pipelines, marking a milestone in technical document processing. https://lnkd.in/dfv7v76Z
Improving Retrieval Augmented Generation: A Step-by-Step Evaluation of RAG Pipelines
pondhouse-data.com
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Helping Startups and SMBs navigate the world of AI and data // I write about tips and tricks around AI, LLMs and data
Creating a RAG system to integrate knowledge with LLMs is one thing - making sure it consistently delivers high-quality answers, does not degrade with additionally added documents and making sure newly added features provide the value hoped for is a totally different story. And quite a hard one. Using a framework called 'ragas' helps to achieve this quite challenging task and enhances overall RAG and AI system performance. Read more about it in Pondhouse Data OG latest blog about RAG evaluation.
📖 New Guide: How to ensure RAG quality 📖 At Pondhouse Data OG, we are committed to building the highest quality AI document systems for technical documents. RAG is revolutionizing how AI models integrate external knowledge, producing more accurate and contextually rich responses. Yet, the journey to optimizing these pipelines is filled with challenges. That's where ragas comes into play. Providing a structured framework for evaluating RAG systems, ragas empowers researchers and developers to fine-tune their implementations, pushing the boundaries of AI capabilities. Our next blog explores the mechanics of RAG and the pivotal role of ragas in their enhancement but also offers a comprehensive guide on setting up and leveraging ragas for significant improvements. With modules like TestsetGenerator and evaluate, ragas is poised to ensure the relevance, accuracy, and efficiency of RAG pipelines, marking a milestone in technical document processing. https://lnkd.in/dfv7v76Z
Improving Retrieval Augmented Generation: A Step-by-Step Evaluation of RAG Pipelines
pondhouse-data.com
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A Comprehensive Study by BentoML on Benchmarking LLM Inference Backends: Performance Analysis of vLLM, LMDeploy, MLC-LLM, TensorRT-LLM, and TGI Quick read: https://lnkd.in/gxPzi8Va BentoML #ai
A Comprehensive Study by BentoML on Benchmarking LLM Inference Backends: Performance Analysis of vLLM, LMDeploy, MLC-LLM, TensorRT-LLM, and TGI
https://www.marktechpost.com
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"Artificial intelligence is too important a technology to be evaluated on the basis of vibes." NYT tech columnist Kevin Roose says that until we develop reliable ways to measure #AI capabilities, we won't know its true risks. #SpeakersOfSubstance #ArtificialIntelligence #AIMeasurement #AIRisk #KeynoteSpeaker #SpeakersBureau #EventProfs https://hubs.la/Q02tcdrX0
A.I. Has a Measurement Problem
https://www.nytimes.com
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Structure-Aware Multi-objective Metaprompt Optimization (SAMMO) framework is a new open-source tool that streamlines the optimization of prompts, particularly those that combine different types of structural information like RAG. It can make structural changes, such as removing entire components or replacing them with different ones. These features enable AI practitioners and researchers to efficiently refine their prompts with little manual effort.
Automating prompt engineering through structural optimization
https://www.microsoft.com/en-us/research
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