RAG - augment

What is RAG - augment ?

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.

Features

• 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.

How to use RAG - augment ?

1. Provide a Query: Input your specific query or prompt for analysis.
2. Retrieve Documents: Use the tool to fetch relevant documents from your dataset.
3. Rerank Documents: Apply RAG - augment to rerank the documents based on their relevance to your query.
4. Generate Output: Use the reranked documents to generate a more accurate and contextually relevant response.
5. Refine Results: Optionally, refine the output by adjusting parameters or rerunning the analysis.

Frequently Asked Questions

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.