Tutorials Prompt Engineering Tutorial
Vector Databases — Complete Guide
Vector Databases — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
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Prompt Engineering Tutorial · Lesson 43 of 100
Vector Databases
Prompts → Apps
Prompts · 1 — Basics · ~6 min · Module 5: RAG Systems
What is this?
Vector databases store embeddings and run fast similarity search (ANN) — Pinecone, pgvector, Qdrant, Weaviate, etc.
Why should you care?
PromptVerse tenants choose Pinecone or Azure AI Search vector fields for production RAG.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
await pineconeIndex.upsert([{
id: "pol-12-chunk-3",
values: embeddingVector,
metadata: { doc_id: "pol-12", title: "Refunds", tenant_id: "acme" }
}]);
const hits = await index.query({ vector: queryVec, topK: 5, filter: { tenant_id: "acme" } });What happened?
- Upsert stores vector + metadata.
- Query filters by tenant_id so Acme never sees Beta docs.
- topK limits prompt size.
Practice next
- Create free tier index.
- Upsert 10 chunks.
- Query with natural language.
- Add hybrid metadata tag product=enterprise.
- Compare pgvector vs managed Pinecone latency.
Remember
ANN search at scale. Metadata filters for multi-tenant. topK balances recall vs tokens.
Multi-tenant index
SaaS hosts 200 customers.
Outcome: tenant_id filter on every query prevents data bleed.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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