Vector Databases for Beginners
What vector databases actually do, and when you need one versus a simpler search approach.
Not scheduled yet — no date, time or fee fixed. The most-voted topic is hosted next.
1:1 or squad batch · join the community or channel
Learn this topic with a mentor
Don't want to wait for the webinar? Pick how you'd like help. Nothing is paid now — you see the fee before anything is booked.
- Bring your own work — code, campaign, report or plan
- Mentor matched to this topic
- Time, format and fee confirmed by email first
What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1What vectors and embeddings represent
- 2How similarity search works
- 3What a vector database adds over a normal database
- 4Popular options and how to choose
- 5Storing, indexing and querying vectors
- 6When you don't need a vector database
Who it's for
- • Developers new to AI data infrastructure
- • Technical learners
You'd leave with
- An understanding of vector search
- Selection criteria for databases
- Basic usage patterns
More topics in Data, RAG & Knowledge AI
Build an AI Chatbot Using Your Own Data
Building a chatbot that answers from your own documents instead of general knowledge.
0 votes · View topic →
RAG Explained Through a Real Project
How retrieval-augmented generation works, explained by building one small real project end-to-end.
0 votes · View topic →
Build a PDF Question-Answering AI
Building a system that answers questions directly from a set of PDF documents.
0 votes · View topic →
Create an AI Knowledge Base for Your Company
Turning existing company documents into a searchable AI-powered knowledge base.
0 votes · View topic →
Embeddings Explained Simply
What embeddings are and how they're used for search and retrieval, explained without heavy math.
0 votes · View topic →
RAG vs Fine-Tuning
When RAG is the better fit versus fine-tuning, based on the actual problem you're solving.
0 votes · View topic →