Vector Databases & Rag Build Semantic Search With Llms

What you'll learn

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Convert raw text and documents into high-dimensional vector embeddings.
Deploy and query vector databases for blazing-fast semantic search.
Build a complete Retrieval-Augmented Generation (RAG) pipeline from scratch.
Connect vector databases to Large Language Models to eliminate hallucinations.
Requirements

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Basic understanding of Python programming and familiarity with making simple API calls. Absolutely no heavy math, linear algebra, or machine learning background is required!
Description

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"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

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ython and software developers entering AI application developmentData and ML engineers new to vector retrievalDevelopers building semantic search or RAG applicationsTechnical product builders evaluating vector-database technologyStudents who have followed AI tutorials but want to understand the underlying retrieval architectureWhat you will learn:Explain why semantic retrieval uses vector representationsGenerate and inspect text embeddingsCompare cosine similarity, dot product, and Euclidean distanceExplain exact and approximate nearest-neighbor retrievalStore vectors with IDs, content references, and metadataExecute top-k semantic searchesApply metadata filters to retrievalExplain how vector retrieval connects to RAG and LLM applicationsEvaluate retrieval using a repeatable query test set and Recall@KCompare vector-database approaches using technical and operational requirementsRequirements: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.
Python developers, data engineers, and product builders looking to transition into AI engineering, build highly accurate RAG applications, and overcome LLM hallucinations.
Homepage

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