An AI framework for retrieving facts from an external knowledge base to ground large language models (LLMs) on the most accurate, up-to-date information and improve the quality of LLM-generated responses.
06 October 2025
Event: Conference
Location : Orange County Convention Center, 9400 Universal Blvd., Orlando, 32819, United States
03 February 2025
Article
Discover effective chunking strategies for improved data retrieval in RAG systems, boosting efficiency in AI, NLP, and machine learning applications.
30 January 2025
Article
Discover how AI integration with OpenPages, watsonx.ai, and Watson Discovery transforms risk management, compliance, and governance in the banking sector.
21 January 2025
Blog
Discover the top 10 most popular blogs, articles, and tutorials for 2024's hottest topic: generative AI.
17 January 2025
Tutorial
This tutorial will show you an implementation of Agentic Retrieval-Augmented Generation (RAG). It can perform multi-step workflows like combining document search and web search to perform complex tasks like business research, feature comparison, news retrieval based on projects, personal knowledge management, and more.
13 December 2024
Article
The article discussed RAG systems, highlighting the benefits, challenges, and optimization strategies for RAG systems by using IBM watsonx capabilities.
10 December 2024
Article
In this article, we explore various optimization techniques and discover practical strategies for taking your RAG implementations to the next level in terms of performance and impact.
03 December 2024
Article
While fine tuning focuses on shaping the model's responses and behavior, RAG relies on integrating external data into the model's workflow. Both approaches customize LLM behavior and output, but each is uniquely suited to different use cases and types of data.
21 November 2024
Blog
The IBM RAG Cookbook is a comprehensive collection of best practices, considerations, and tips for building RAG solutions tailored to business applications.
14 November 2024
Article
Learn about the benefits of a branched RAG system, including improving the accuracy of complex queries, handling ambiguity, and providing more precise retrieval of relevant information.