Category: AI

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Avatar assisted & dialogue driven voice to RAG search

Create avatar-assisted voice search experience by integrating speech-to-text, semantic search, RAG and a synthesized avatar for responses.

Sunile Manjee

How to build an Elastic search app with Streamlit, semantic search & NER

Learn how to develop a search application using machine learning models for named entity extraction (NER), semantic search and Streamlit.

Camille Corti-Georgiou

RAG evaluation metrics: A journey through metrics

Explore RAG evaluation metrics like BLEU score, ROUGE score, PPL, BARTScore, and more. Discover how Elastic is evaluating RAG with UniEval.

Quentin Herreros

Retrieval Augmented Generation (RAG) using Cohere Command model through Amazon Bedrock and domain data in Elasticsearch

Learn how to implement Retrieval Augmented Generation (RAG) using Cohere Command model via Amazon Bedrock & domain data in Elasticsearch.

Udayasimha Theepireddy

Domain specific generative AI: pre-training, fine-tuning, and RAG

Explore strategies for integrating domain-specific knowledge into large language models (LLMs) through pre-training, fine-tuning, and RAG.

Steve Dodson

Retrieval Augmented Generation (RAG)

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

Joe McElroy

A conversational search experience for retail: Elasticsearch Relevance Engine with Google Cloud’s generative AI

This blog presents a new search experience for retailers using generative AI with Vertex AI and Elasticsearch.

Valerio Arvizzigno

Elasticsearch as a GenAI caching layer

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 examples.

Jeff Vestal

How to Use Amazon Bedrock with Elasticsearch and Langchain

Learn to split workplace documents into passages, transform these passages into embeddings in Elasticsearch and integrate Amazon Bedrock LLM.

Yan Savitski

Generative AI architectures with transformers explained from the ground up

Here's how generative AI works from the ground up, including embeddings, transformer-encoder architecture, training/fine-tuning models & more.

Aris Papadopoulos

Demystifying ChatGPT & LLMs: Different methods for building AI search

Explore the inner workings of ChatGPT and LLMs, and discover three effective approaches for building generative AI search experiences for specific domains.

Sherry Ger

Retrieval vs. poison — Fighting AI supply chain attacks

Learn about the supply chain vulnerabilities of artificial intelligence large language models (LLMs) and how the AI retrieval techniques of search engines can be used to fight misinformation and intentional tampering of AI.

Dave Erickson

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