Retrieval augmented generation — a search problem

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) 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.

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.

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