Improving RAG Applications with Reranker Models #ai #machinelearning

Reranker models can help improve the responses of RAG-based applications. These models offer several key benefits:

1. Improved Content Relevance:
- Sort content by its relevancy to the user's query
- Remove irrelevant content

2. Efficiency Gains:
- Lead to up to an 85% reduction in tokens sent to LLMs for generation

3. Enhanced Performance:
- New reranking endpoints from Pinecone allow co-location with the vector database
- Reduce hops and improve overall performance

4. RAG Application Improvements:
- Reranking has become a common aspect of RAG applications
- Helps reduce the amount of data going into the LLM
- Ultimately improves accuracy and reduces hallucinations

By implementing reranking, developers can create more efficient and accurate RAG-based systems.