Vector Databases & RAG: Build Semantic Search with LLMs

Vector Databases & RAG: Build Semantic Search with LLMs
Published 8/2026
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch

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:
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Python and software developers entering AI application development
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Data and ML engineers new to vector retrieval
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Developers building semantic search or RAG applications
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Technical product builders evaluating vector-database technology
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Students who have followed AI tutorials but want to understand the underlying retrieval architecture
What you will learn:
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Explain why semantic retrieval uses vector representations
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Generate and inspect text embeddings
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Compare cosine similarity, dot product, and Euclidean distance
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Explain exact and approximate nearest-neighbor retrieval
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Store vectors with IDs, content references, and metadata
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Execute top-k semantic searches
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Apply metadata filters to retrieval
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Explain how vector retrieval connects to RAG and LLM applications
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Evaluate retrieval using a repeatable query test set and Recall@K
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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.

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