Optimize Text Embedding Efficiency with Quantization
Explore how Qdrant's Binary Quantization can significantly improve the efficiency and performance of modern text embedding models.
Ewa Szyszka
July 15, 2026
Search Quality
Learn how to evaluate and improve the quality of your vector search. Explore relevance feedback, evaluation methodologies, and benchmarking techniques.
Predicting Weak Retrieval Without an LLM
Detect weak retrieval with cheap signals from the result, no extra model call. Which one predicts failure depends on your corpus, so test it on your data.
Dylan Couzon
June 24, 2026
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
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
Enhance Search Efficiency with 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