Embedding Visualizer
Watch AI understand semantic meaning in real-time
What is this?
This visualization shows 72 portfolio items mapped in semantic space. Each dot represents work experience, projects, skills, or education—converted to 1536-dimensional vectors using OpenAI text-embedding-3-small, then reduced to 2D using PCA (Principal Component Analysis).
How it works: Text → 1536D vectors (OpenAI) → PCA → 2D visualization
→ Try it: Enter any text below to see how it maps against my experience.
This is how RAG (Retrieval-Augmented Generation) systems work under the hood!
How to read:
Points closer together are semantically similar. Note that PCA preserves ~15-30% of the original variance—balancing visualization clarity with implementation simplicity.
See this in action:
Visit the RAG Playground to compare how different retrieval strategies (Basic, Hybrid, Re-ranking) perform on this exact knowledge base! Watch real-time queries use these semantic clusters to retrieve the most relevant documents.
Generating embedding visualization...
Converting >20 knowledge base items into semantic space
Product Evolution
MVP → Validate → Improve → Know when to stop
MVP
Simplified Projection
First 2 dimensions only. Shipped in 1 day to validate concept.
Upgrade
JavaScript PCA
Proper dimensionality reduction using PCA via ml-pca library.
Python UMAP
Best-in-class
UMAP offers better clustering, but adds ~1 week + pipeline complexity.
Product Manager Lesson
Ship fast to validate → Improve when you understand the problem deeply → Stop before over-engineering
PCA preserves ~17% variance vs UMAP's ~25%. Good enough for portfolio demo.
80% of the value, 20% of the complexity.