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

Connected skills

RAGLangChain

Explore Vector Databases in the interactive universe or train with live cohorts.

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