Retrieval-augmented generation, or RAG, integrates external data sources to reduce hallucinations and improve the response accuracy of large language models. Retrieval-augmented generation (RAG) is a ...

We introduce ChronoQA, a benchmark dataset for Chinese question answering focused on evaluating temporal reasoning in Retrieval-Augmented Generation (RAG) systems. Built from over 300,000 news ...

Large language models (LLMs) have significantly advanced in recent years, greatly enhancing the capabilities of retrieval-augmented generation (RAG) systems. However, challenges such as semantic ...

The new service automates embeddings, indexing, and connectors to help developers focus on building AI apps instead of ...

Exploring AI-generated content and professional guidelines in cancer symptom management: A comparative analysis between ChatGPT and NCCN guidelines. Performance of various RAG-LLMs for clinical trial ...

Google Ad Manager AI agent Ask Ad Manager launches in beta this month, using Gemini and retrieval-augmented generation over ...

Millions of people rely on AI assistants every day to retrieve facts, diagnose problems, and summarize the news. But what ...

Learn how enterprises can scale AI infrastructure by aligning servers, storage, networking, and governance to avoid costly ...

MIT's MeMo keeps AI memory separate from reasoning, so teams can upgrade their LLM without retraining and see a 26% performance gain, researchers say.

Retrieval triggers when a user prompt demands information the model cannot reliably generate from memory alone: current ...

AI has transformed the way companies work and interact with data. A few years ago, teams had to write SQL queries and code to extract useful information from large swathes of data. Today, all they ...