Vector Databases & RAG: Build Semantic Search with LLMs

About this course

“This course contains the use of artificial intelligence.”

Stop copying RAG code without understanding the retrieval system underneath it.

Vector databases are a foundational technology for semantic search, retrieval-augmented generation, recommendations, similarity matching, and other modern AI applications. This course gives you a practical, vendor-neutral introduction to the concepts that make those systems work.

You'll progress from vectors and embeddings through similarity measurement, nearest-neighbor retrieval, metadata filtering, vector indexes, RAG architecture, database selection, and retrieval evaluation.

Who this course is for:

• Python and software developers entering AI application development
• Data and ML engineers new to vector retrieval
• Developers building semantic search or RAG applications
• Technical product builders evaluating vector-database technology
• Students who have followed AI tutorials but want to understand the underlying retrieval architecture

What you will learn:

• Explain why semantic retrieval uses vector representations
• Generate and inspect text embeddings
• Compare cosine similarity, dot product, and Euclidean distance
• Explain exact and approximate nearest-neighbor retrieval
• Store vectors with IDs, content references, and metadata
• Execute top-k semantic searches
• Apply metadata filters to retrieval
• Explain how vector retrieval connects to RAG and LLM applications
• Evaluate retrieval using a repeatable query test set and Recall@KCompare vector-database approaches using technical and operational requirements

Requirements:

Basic Python programming is recommended. Familiarity with APIs and conventional databases will help. No advanced mathematics, machine-learning background, or prior ve

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