RAG and vector databases help AI systems retrieve relevant information from documents, databases and knowledge bases before generating an answer.
RAG and Vector Databases courses
View all →RAG and Vector Databases tutorials
All tutorials related to this topic, gathered in one place.
Criteria: same topic and editorial language. Tutorials are individual content and do not represent a required sequence.
2h 33min
ENLearn RAG From Scratch – Python AI Tutorial from a LangChain Engineer
18min
ENOpenAI Embeddings and Vector Databases Crash Course
9min
ENWhat is a Vector Database? Powering Semantic Search & AI Applications
10min
ENRAG Explained For Beginners
12min
ENRAG Explained in 12 Minutes
1h 11min
ENVector Search RAG Tutorial – Combine Your Data with LLMs with Advanced Search
2h 08min
ENComplete RAG Crash Course With Langchain In 2 Hours
17min
ENWhat is RAG ? | Completely Explained in 15 Minutes
5h 40min
ENLocal Retrieval Augmented Generation (RAG) from Scratch (step by step tutorial)
1h 36min
ENRAG Fundamentals and Advanced Techniques – Full Course
Frequently asked questions about RAG and Vector Databases
What is RAG and Vector Databases?
RAG and Vector Databases is a technology topic that learners can study through concepts, practical workflows and validation habits.
Why should I learn RAG and Vector Databases now?
It is connected to current AI, software, security and infrastructure changes, so it helps learners understand where modern technology work is moving.
Is RAG and Vector Databases beginner-friendly?
Yes, when studied in order. Start with conceptual lessons, then move into practical tutorials and deeper technical material.
Do I need to know programming?
Some lessons are useful without programming, but developer-focused material may require basic Python, APIs, command-line tools or web concepts.
What should I practice first?
Start with a small task that can be repeated and checked, then change one variable at a time to understand the workflow.
How does this connect with AI agents?
Many modern technology topics connect with agents through tools, retrieval, evaluation, security, automation or developer workflows.
How do I validate what I learn?
Check sources, run code when available, compare outputs, document assumptions and test whether the result solves the original task.
What comes after this topic?
The next step is usually a project: build a small workflow, test it, document the result and connect it with related Cursob topics.
How were these materials selected?
The selection prioritizes free, embeddable English videos with practical value, clear explanations and relevance to the topic.
How to study RAG and Vector Databases without getting lost
The best way to learn RAG and Vector Databases is to separate concepts, tools, practical examples and validation. This keeps the topic useful even when individual tools change.
Start from the problem
Before choosing a tool, identify the problem the technology is trying to solve. Write down the inputs, expected output, constraints and risks. This makes each tutorial easier to compare.
Practice with small workflows
Use a small example that can be repeated. Change one part of the workflow, inspect the result and keep notes about what improved or failed.
Validate before scaling
Modern AI and infrastructure workflows can look impressive before they are reliable. Review claims, test code, check permissions and document assumptions before applying the method to important work.