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Datacloud: RAG Retrievers Helps Business to Unlock Unstructured Data
This post educates on two advanced search techniques that leverage artificial intelligence for enhanced search capabilities.
Semantic Search/Vector Search is an AI-driven method that goes beyond traditional keyword matching. It focuses on comprehending the context and purpose behind a user's search query, leading to more precise and relevant results.
Retrieval Augmented Generation (RAG) in Data Cloud introduces a framework designed to enhance large language model (LLM) prompts. By enriching prompts with up-to-date and relevant data, RAG elevates the accuracy and usefulness of LLM responses, providing users with more valuable information.
When you submit an LLM prompt, RAG in Data Cloud:
Retrieves relevant information from structured and unstructured data indexed in Data Clouds vector database Augments the prompt by combining this information with the original prompt Generates a prompt response
Semantic Search/Vector Search is an AI-driven method that goes beyond traditional keyword matching. It focuses on comprehending the context and purpose behind a user's search query, leading to more precise and relevant results.
Retrieval Augmented Generation (RAG) in Data Cloud introduces a framework designed to enhance large language model (LLM) prompts. By enriching prompts with up-to-date and relevant data, RAG elevates the accuracy and usefulness of LLM responses, providing users with more valuable information.
When you submit an LLM prompt, RAG in Data Cloud:
Retrieves relevant information from structured and unstructured data indexed in Data Clouds vector database Augments the prompt by combining this information with the original prompt Generates a prompt response
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