Mastering Rerankers for AI Vector and Hybrid Search Accuracy
Struggling with search accuracy in your AI app? Rerankers can boost results by 10-20%—but when should you use them? Join us to learn how Hybrid, Lexical, and Multi-Vector Search benefit from Rerankers, with real-world examples and live Q&A.
Not getting the best search results from your AI application? Rerankers can boost RAG accuracy by 10-20%—not just for hybrid queries but for any vector or non-vector search! But when and how should you use them?
Join us for an AI Architect Series Special with Brian O’Grady & Samuel Matioli, where we’ll break down how Rerankers improve Hybrid Search, Lexical Search, and Multi-Vector Search—and show you how to implement them in real-life, production use cases.
What You’ll Learn:
- What are Rerankers? How they work in the Data API
- When to Use Them: The difference between hybrid, lexical, and multi-vector search
- How to Implement Rerankers: A deep dive into query patterns Astra DB supports
Afterward, we'll have time for Q&A so bring your questions on rerankers, search patterns, and multi-vector retrieval. If you can't join live, register anyway and we'll share the replay!
Samuel Matioli
Solution Engineer
Langflow
Brian O'Grady
AI Architect
DataStax
