Learn about how to prompt Command R: Understand the structured prompts used for RAG, formatting chat history and tool outputs, and changing sections of the prompt for different tasks.
A CLI tool for interacting with local or remote LLMs to retrieve information about files, execute queries, and perform other tasks in a Retrieval-Augmented Generation (RAG) fashion.
LlamaIndex comes with a built-in indexing feature, which allows developers to index large datasets efficiently. This makes it easier to search and retrieve information from these datasets, ultimately improving the overall performance of LLM-based applications.
This article discusses how to overcome limitations of retrieval-augmented generation (RAG) models by creating an AI assistant using advanced SQL vector queries. The author uses tools such as MyScaleDB, OpenAI, LangChain, Hugging Face and the HackerNews API to develop an application that enhances the accuracy and efficiency of data retrieval process.
This article discusses the potential of unstructured data in software development and how it can be leveraged using Retrieval-Augmented Generation (RAG). The article highlights various types of unstructured data on GitHub, the value of unstructured data, and how RAG can be used to extract insights from this data. The article also explains how RAG works, its applications, and its benefits for developers and organizations.
Case study on measuring context relevance in retrieval-augmented generation systems using Ragas, TruLens, and DeepEval. Develop practical strategies to evaluate the accuracy and relevance of generated context.
Simon Willison explains an accidental prompt injection attack on RAG applications, caused by concatenating user questions with documentation fragments in a Retrieval Augmented Generation (RAG) system.
The technology of retrieval-augmented generation, or RAG, could be pivotal in shaping the battle between large language models.
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.
This article discusses GNN-RAG, a new AI method that combines the language understanding abilities of LLMs with the reasoning abilities of GNNs for Retrieval-Augmented Generation (RAG) style. This approach improves KGQA performance by utilizing GNNs for retrieval and RAG for reasoning.
