Relevance Feedback in Qdrant
The story behind the vector search-native relevance feedback feature, available since 1.17.0, which increases the relevance of search results universally, cheaply, and at scale.
Evgeniya Sukhodolskaya
February 20, 2026
Search Quality
Learn how to evaluate and improve the quality of your vector search. Explore relevance feedback, evaluation methodologies, and benchmarking techniques.
Relevance Feedback in Informational Retrieval
Relerance feedback: from ancient history to LLMs. Why relevance feedback techniques are good on paper but not popular in neural search, and what we can do about it.
Evgeniya Sukhodolskaya
March 27, 2025
Optimizing RAG Through an Evaluation-Based Methodology
Learn how Qdrant-powered RAG applications can be tested and iteratively improved using LLM evaluation tools like Quotient.
Atita Arora
June 12, 2024
Optimizing OpenAI Embeddings: Enhance Efficiency with Qdrant's Binary Quantization
Explore how Qdrant's Binary Quantization can significantly improve the efficiency and performance of OpenAI's Ada-003 embeddings. Learn best practices for real-time search applications.
Nirant Kasliwal
February 21, 2024
