Vector Databases and RAG Course

Core Concepts

RAG learning should cover chunking, embeddings, vector indexes, metadata filters, hybrid search, reranking, citations, and answer evaluation.

Application Patterns

Common RAG applications include documentation search, customer support, policy assistants, research tools, and internal knowledge copilots.

Quality Controls

Good RAG systems need source grounding, answer constraints, retrieval tests, freshness handling, and clear failure states.

Frequently Asked Questions

What is RAG in AI?

RAG means retrieval augmented generation. It retrieves relevant information and gives it to a generative model so answers are more grounded.

Why use vector databases for AI?

Vector databases help AI applications find semantically similar content through embeddings, which is useful for search, recommendations, and RAG.

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