Vector Databases
Stores and queries embeddings for semantic search and RAG.
Maturity level: L2 — Can Build
Six perspectives on Vector Databases
Roadmap
Learn alongside or immediately after RAG fundamentals.
Architecture
Persistence layer between embeddings and retrieval API.
Company
Common in GenAI and LLMOps job descriptions.
Projects
Integrate ChromaDB or pgvector in a RAG capstone.
Interview
Indexing, recall, and operational concerns.
Career
Required for production LLM applications.
What & Why
What: Databases optimized for similarity search over high-dimensional vectors.
Why: Enables fast retrieval for RAG, recommendations, and semantic search.
Build this
Vector index with hybrid keyword + semantic search.
Production reality
- ! Index rebuild times
- ! Recall vs latency tradeoffs
- ! Embedding model changes invalidating indexes
Interview preparation
- When to use vector DB vs traditional search?
- Hybrid retrieval strategies
Explore Vector Databases in the interactive universe or train with live cohorts.
Want this Vector Databases learning path turned into your own plan?
A roadmap shows the destination. In 1:1 mentorship I turn it into a week-by-week learning path for your background, review your projects, and prepare you for interviews until you land the role. Online, in your time zone.
Start with one 60-minute 1:1 call: .
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