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.
Learn more about Elastic's latest innovations to scale generative AI use cases.
Read blogBuild RAG into your apps, try different LLMs with a vector database.
Discover more on Elasticsearch labsLearn about how to build advanced RAG-based applications using the Elasticsearch Relevance Engine™.
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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
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.
HOW RAG WORKS WITH ELASTIC
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.
USE CASE
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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