Vector Databases & RAG: Build Semantic Search with LLMs

“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@K

• Compare 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 vector-database experience is required.

Final project:
You will build a portfolio-ready semantic retrieval system containing at least 100 records. Your solution will create embeddings, store vectors and metadata, execute top-k similarity searches, support metadata filtering, and undergo evaluation with at least 10 test queries. You will finish with a README, architecture diagram, evaluation results, sample queries, failure analysis, and technology decision that you can discuss with an employer or client.