Generative AI

There are a number of strategies to add domain specific knowledge to large language models (LLMs) such as pre-training, fine-tuning Models and using Retrieval Augmented Generation (RAG).

What is Retrieval Augmented Generation (RAG) and how the technique can help improve the quality of an LLM's generated responses, by providing relevant source knowledge as context.

Explore how integrating Elasticsearch as a caching layer optimizes Generative AI performance by reducing token costs and response times, demonstrated through real-world testing and practical implementations.

This long-form article explains how generative AI works, from the ground all the way up to generative transformer architectures with a focus on intuitions.

In this blog, we look at how ChatGPT works, and consider three approaches to build generative AI like search experiences for specific domains.

In this post, learn about the supply chain vulnerabilities of artificial intelligence large language models and how the AI retrieval techniques of search engines can be used to fight misinformation and intentional tampering of AI.

Learn how to build a GAI solution by exploring Amazon SageMaker JumpStart, Elastic, and Hugging Face open source LLMs using the sample implementation provided in this post and a data set relevant to your business.

Elasticsearch Relevance Engine is a set of tools for developers to build AI-powered search applications. Relativity, the eDiscovery and legal search tech company, is building next-generation search experience with Elastic and Microsoft Azure Open AI.

Learn how Elastic is at the forefront of the Large Language Models revolution –– helping users take LLMs to new heights by providing real-time information and integrating LLMs into search, observability, and security systems for data analysis.

This blog presents a novel application of the Elasticsearch Relevance Engine (ESRE) with its Elastic Learned Sparse Encoder capability, specifically in log analysis.

In this blog, we'll instrument a Python application that uses OpenAI and analyze its performance, as well as the cost to run the application. Using the data gathered from the application, we will also show how to integrate LLMs into your application.

By providing tools like ChatGPT additional context, you can increase the likelihood of obtaining more accurate results. See how Elasticsearch's faceting and filtering framework can allow users to refine their search and reduce costs.

Explore the integration of Elasticsearch's search relevance capability with ChatGPT's question-answering capability to enhance your domain-specific knowledge base. Learn how to harness ChatGPT to enrich your information repository like never before!

Learn how to implement a plugin and enable ChatGPT users to extend ChatGPT with any content indexed in Elasticsearch, using the Elastic documentation.

Get ready to be blown away by this game-changing approach to monitoring cutting-edge ChatGPT applications! As the ChatGPT phenomenon takes the world by storm, it's time to supercharge your monitoring game with OpenTelemetry and Elastic Observability.