Leverage Qdrant for Retrieval-Augmented Generation (RAG) and build AI Agents
Two emerging patterns for agent-assisted development: static knowledge files and dynamic tool access. How they complement each other using Qdrant as a case study.
Thierry Damiba
January 28, 2026
Learn how to build performant, scalable AI agents with efficient vector retrieval, hybrid dense-sparse search, real-time memory, multimodal context integration, and optimized architectures for low-latency, high-accuracy execution in production environments.
Thierry Damiba
October 26, 2025
Agents are a new paradigm in AI, and they are changing how we build RAG systems. Learn how to build agents with Qdrant and which framework to choose.
Kacper Łukawski
November 22, 2024
Learn how Qdrant-powered RAG applications can be tested and iteratively improved using LLM evaluation tools like Quotient.
Atita Arora
June 12, 2024
Semantic cache is reshaping AI applications by enabling rapid data retrieval. Discover how its implementation benefits your RAG setup.
Daniel Romero, David Myriel
May 07, 2024
Explore how RAG enables LLMs to retrieve and utilize relevant external data when generating responses, rather than being limited to their original training data alone.
Sabrina Aquino
March 19, 2024
Uncover the necessity of vector databases for RAG and learn how Qdrant's vector database empowers enterprise AI with unmatched accuracy and cost-effectiveness.
David Myriel
February 27, 2024
