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RAG - augment is a text analysis tool that leverages Retrieval-Augmented Generation (RAG) technology. It integrates document retrieval with generative AI to enhance the quality and relevance of text outputs. By reranking documents based on a query, RAG - augment ensures more accurate and contextually appropriate results.
• Advanced Relevance Ranking: Reranks documents to prioritize those most relevant to the query. • Integration with Generative Models: Works seamlessly with generative AI to improve output quality. • Real-Time Processing: Delivers results efficiently, even for complex queries. • Scalable Architecture: Designed to handle large datasets and high-volume requests.
What is RAG - augment used for?
RAG - augment is primarily used to enhance the accuracy and relevance of text generation by leveraging document retrieval and reranking.
How does RAG - augment improve results?
By reranking documents based on query relevance, RAG - augment ensures that the most relevant information is used for generating outputs.
Does RAG - augment require training data?
No, RAG - augment works with pre-existing datasets and does not require additional training data to function effectively.