Introduction to Vector Databases for Developers Micro-Credential

Introduction to Vector Databases for Developers Micro-Credential

Duration: 3-4 Hours

Description

Vector databases are the layer that makes RAG, semantic search, and “chat with your documents” features possible, but most developers use one without ever understanding what it’s actually doing. This session opens that up: what a vector and a similarity search actually are, how to generate embeddings from real text, and how to build and query a vector database directly. Labs are deliberately concrete — participants set up a working vector store and build a small Q&A bot on top of it, so the concepts land as something built, not just explained.

Audience

This session is for developers with some programming experience (Python is the most common fit) who keep encountering vector databases in AI tooling and want to actually understand the layer, not just call an API against it. No prior AI or machine learning background is assumed.

Objectives

  • Understand what a vector database is and how it differs from a traditional database
  • Explain the concept of vector embeddings and their importance
  • Use a vector database to perform a similarity search
  • Identify common use cases for vector databases, such as RAG and recommendation systems

Prerequisites

Basic familiarity with programming (such as Python) and a general understanding of databases or data structures is recommended. No prior experience with AI, machine learning, or vector search is required, making this program accessible to developers, data scientists, and technical professionals new to vector-based systems.

Related AI Courses

See the full AI training roadmap and course directory for how this fits into a broader learning path.

For the full multi-day course, see Vector Databases or Vector Databases and Semantic Search. Ready to build a full retrieval pipeline on top of this? See Beginner’s Guide to RAG Micro-Credential or RAG Deep Dive.

Course Outline

Module 1: Beyond Relational Databases

  • The Problem with Unstructured Data
  • What is a Vector?
  • What is a Vector Database?
  • Lab – Create Vector Embeddings from Different Types of Text

Module 2: Building with Vectors

  • The Core Workflow
  • Similarity Search in Action
  • Vector Databases in RAG
  • Lab – Set Up a Memory Vector Database

Module 3: From Theory to Practice

  • Common Vector Databases
  • Key Metrics
  • Scaling and Production
  • Lab – Q&A Bot