Grow as a Search Engineer

Make better search decisions, adapt a working example, or understand how Qdrant works beneath the API.

Choose the resource that answers your question today.

Choose How to Learn

Each resource serves a different purpose. Start with the one that fits your task.

Evaluate search quality, choose embedding models, and plan how your Qdrant application grows.

Open working code and walkthroughs, then adapt the implementation to your data and application stack.

Build your understanding through structured lessons and exercises, starting with Qdrant Essentials.

Explore vector search concepts, retrieval research, and the mechanisms behind Qdrant indexing, storage, and search.

Take a direct path to a common search engineering task.

Start with a small semantic search application and adapt it to your own data.

Choose an evaluation baseline before changing embeddings, retrieval, or ranking.

Plan tenant growth and large imports around the workload you need to serve.

Choose embedding models, plan query decomposition, and filter results for your application.

Follow the retrieval tuning series to test candidate depth, fusion, reranking, and memory choices.

Learn from video walkthroughs, technical talks, and conversations with the Qdrant team.