AI Quick Reference
Practical references for vector data lakes, Milvus components, retrieval indexes, AI agents, data curation, and production systems. Browse questions by topic.
- What is retrieval-augmented generation (RAG)?
- How do you choose chunk size for RAG?
- How do you evaluate retrieval quality?
- How should you structure agent memory in Milvus?
- What is Graph RAG, and where does Milvus fit?
- What is agentic search, and where does a vector database fit?
- How should an agent choose between vector, keyword, and structured search?
- How do working, episodic, semantic, and procedural agent memory differ?
- How should an agent memory system handle corrections, expiry, and deletion?
- How can vector search retrieve useful agent trajectories?
- How should you select rollouts for an offline agent-training dataset?
- When is a semantic cache useful, and how do you prevent stale answers?
- How do you build an evaluation set for agentic and vector search?