This blog post demonstrates how to create a reusable retrieval evaluation dataset using an LLM to judge query-document pairs. It discusses the process, including building a small labeled dataset, aligning LLM judgments with human preferences, and using the LLM to judge a large set of queries and documents.
This article discusses the integration of Large Language Models (LLMs) into Vespa, a full-featured search engine and vector database. It explores the benefits of using LLMs for Retrieval-augmented Generation (RAG), demonstrating how Vespa can efficiently retrieve the most relevant data and enrich responses with up-to-date information.
