Elastic Playground facility reduces hallucinations in LLMs #MentalHealth

Elastic, a data search supplier, has introduced a Playground facility to help developers improve the accuracy of large language models (LLMs) by adding proprietary data and conducting LLM/RAG testing. This allows developers to bring their proprietary data indexed in Elasticsearch to LLMs for retrieval-augmented generation (RAG) of model responses. The process involves iterative testing and development of RAG/LLM combos by comparing response results from different versions of LLMs and tuning prompts for further testing.

The Playground operates within an Elasticsearch environment based on Elastic’s AI platform, which includes a vector database. Developers can use a low-code interface to access hybrid search capabilities, combining vector-based and plain text-based search. LLMs can be run directly within the AI platform or through an Open Inference API that supports integration with various inference providers.

The goal of the Playground is to improve the accuracy and reliability of LLM responses, ultimately accelerating the time to market for customers. The use of RAG techniques aims to eliminate false information and ensure complete and accurate responses from LLMs. Elastic’s Playground will help demonstrate the effectiveness of RAG techniques in enhancing LLM performance and promoting their use among Elastic’s customer base.

For more information on how to use the Playground, Elastic has provided a detailed blog post outlining the process.

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Source link: https://blocksandfiles.com/2024/07/04/elastic-playground/