Etiqueta: RAG retrieval-augmented generation
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Build Performant RAG Applications Using Couchbase Vector Search and Amazon Bedrock
Generative AI (GenAI) has the potential to automate work activities that currently occupy 60 to 70 percent of employees’ time, leading to substantial productivity gains across various industries. However, a General Purpose (GP) LLM’s…
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A Step-by-Step Guide to Preparing Data for Retrieval-Augmented Generation (RAG)
In today’s data-driven world, the ability to efficiently gather and prepare data is crucial for the success of any application. Whether you’re developing a chatbot, a recommendation system, or any AI-driven solution, the quality…
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Enhancing GenAI for Privacy and Performance: The Future of Personalized AI with Edge Vector Databases
The evolution of Generative AI (GenAI) is marked by a significant transition from model development to application development. As these AI models mature, the focus shifts to integrating them into real-world applications, bringing about…
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Develop Performant RAG Apps With Couchbase and Vectorize
For technology leaders and developers, the process of integrating rich, proprietary data into generative AI applications is often filled with challenges. Vector similarity search and retrieval augmented generation are powerful tools to help with…
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What are Foundation Models? (Plus Types and Use Cases)
What is a Foundation Model? A foundation model is a powerful type of artificial intelligence (AI) trained on massive amounts of general data, allowing it to tackle a broad range of tasks. Foundation models,…
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An Overview of Retrieval-Augmented Generation (RAG)
What Is Retrieval-Augmented Generation? There’s no doubt that large language models (LLMs) have transformed natural language processing, but at times, they can be inconsistent, random, or even plain wrong in the responses they deliver…