Tag: RAG retrieval-augmented generation
A Guide to Data Chunking
What is Data Chunking? Data chunking is a technique that breaks down large datasets into smaller, more manageable chunks. It’s crucial to artificial intelligence, big data analytics, and cloud computing because it optimizes memory usage, speeds up processing, and improves...
Supercharge Your RAG application With Couchbase Vector Search and Unstructured.io
Today we’re excited to announce the launch of the Couchbase and Unstructured.io connector which streamlines the process of ingesting unstructured data into your RAG pipeline built on top of Couchbase as the vector store. Using this connector, you can now...
Preparing Datasets for Fine-Tuning ML Models: A Comprehensive Guide
Fine-tuning machine learning models starts with having well-prepared datasets. This guide will walk you through how to create these datasets, from gathering data to making instruction files. By the end, you’ll be equipped with practical knowledge and tools to prepare...
Building End-to-End RAG Applications With Couchbase Vector Search
Large Language Models, popularly known as LLMs is one of the most hotly debated topics in the AI industry. We all are aware of the possibilities and capabilities of ChatGPT by OpenAI. Eventually using those LLMs to our advantage discovers...
Building a Path to Edge AI for Vector Search, Image, and Data Focused Applications
We continue to hear from customers that they see the immense value and importance of artificial intelligence (AI), generative AI, vector search, and edge computing. These technologies are becoming more critical to collect data and provide actionable insights. At the...
Vector Search at the Edge with Couchbase Mobile
We’re pleased to announce the release of Couchbase Lite 3.2 with support for vector search. This launch follows the coattails of vector search support on Capella and Couchbase Server 7.6. Now, with vector search support in Couchbase Lite, we enable...
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 knowledge is confined to its training...
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 and structure of your data can...
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 new challenges. Application developers and infrastructure...
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 this, but one mistake extracting, chunking,...
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, such as OpenAI’s GPT (Generative Pre-trained...
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 to a prompt. While this can...