Retrieval augmented generation — a search problem

Search is critical infrastructure for working with large language models (LLMs) to build the best generative AI experiences. You get one chance to prompt an LLM to deliver the right answer with your data, so relevance is essential. Ground your LLMs with retrieval augmented generation using Elastic.

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Learn more about Elastic's latest innovations to scale generative AI use cases.

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Build RAG into your apps, try different LLMs with a vector database.

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Learn about how to build advanced RAG-based applications using the Elasticsearch Relevance Engine™.

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The Elastic advantage

Production ready for enterprise scale

  • Accelerating generative AI experiences

    Roll out your generative AI experiences fast and at scale, with Elasticsearch.

  • The most relevant search engine for RAG

    Stay relevant with cutting edge search techniques (textual, semantic, vector, hybrid), integrated reranking tools and Learning to Rank (LTR).

  • Model selection made easy

    Streamline model selection and management with our open platform, for efficient, effective, and future-proof RAG implementations.


Make your data ready for RAG

RAG extends the power of LLMs by accessing relevant proprietary data without retraining. When using RAG with Elastic, you benefit from:

  • Cutting-edge search techniques
  • Easy model selection and the ability to swap models effortlessly
  • Secure document and role-based access to ensure your data stays protected
Retrieval augmented generation (RAG) in action

Transform search experiences

What is retrieval augmented generation?

Retrieval Augmented Generation (RAG) is a pattern that enhances text generation by integrating relevant information from proprietary data sources. By supplying domain-specific context to the generative model, RAG improves the accuracy and relevance of the generated text responses.

Use Elasticsearch for high relevance context windows that draw on your proprietary data to improve LLM output and deliver the information in a secure and efficient conversational experience.


Enhance your RAG workflows with Elasticsearch

Discover how using Elastic for RAG workflows enhances generative AI experiences. Easily sync to real-time information using proprietary data sources to get the best, most relevant generative AI responses.

The machine learning inference pipeline uses Elasticsearch ingest processors to extract embeddings efficiently. Seamlessly combining text (BM25 match) and vector (Knn) searches, it retrieves top-scoring documents for context-aware response generation.


Q&A service that runs on your private data set

Implement Q&A experiences using RAG, powered by Elasticsearch as a vector database.

Search — in action

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    Consensus upgrades academic research platform with advanced semantic search and AI tools from Elastic.

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    Cisco creates AI-powered search experiences with Elastic on Google Cloud.

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    Georgia State University increases data insights and explores helping students apply for financial aid with AI‑powered search.