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Philipp Moritz

In this guide, we will learn how to develop and productionize a retrieval augmented generation (RAG) based LLM application, with a focus on scale and evaluation.  

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Building RAG-based LLM Applications for Production

Amog Kamsetty

In part 2 of this blog series, we show you how to turbocharge embeddings in LLM workflows using LangChain and Ray Data.

Embedding - Stages of our Data Pipeline

Turbocharge LangChain: Guide to 20x Faster Embedding

Turbocharge LangChain: guide to 20x faster embedding

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​​Reinventing Multi-Modal Search with Anyscale and MongoDB

Solve resource fragmentation with Anyscale's new Replica Compaction feature.  Easily optimize resource usage for online inference replication.

Anyscale’s New Replica Compaction

Introducing Anyscale’s New Replica Compaction

Deploy Ray Serve with up to 50% fewer nodes using Anyscale Replica Compaction

Anyscale - LLM Router

Building an LLM Router for High-Quality and Cost-Effective Responses

Ray Spotlight

Ray Spotlight: How we delivered Ray weekly releases

Execute end-to-end LLM workflows to develop & productionize LLMs at scale.

End-to-end LLM Guide header

End-to-end LLM Workflows Guide

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