EMBEDDINGS: SEMANTIC UNDERSTANDING
Converting Text to Vectors for AI Applications
By UDDIT, Embedding Models Expert & AI Engineer
WHAT ARE EMBEDDINGS?
Embeddings convert text into numerical vectors that capture semantic meaning. They are the foundation of RAG systems, semantic search, and modern AI applications. The quality of your embeddings directly determines the quality of your AI application's understanding.
✎ EDITOR'S NOTE
"Embeddings are the secret sauce that enables AI to understand meaning, not just keywords."
HOW EMBEDDINGS WORK
- ■ Text input is tokenized
- ■ Neural network processes tokens
- ■ Output: dense vector (e.g., 1536 dims)
- ■ Similar meanings = similar vectors
- ■ Enables similarity search
POPULAR EMBEDDING MODELS
OpenAI text-embedding-3-large
3072 dimensions, state-of-the-art accuracy
Best for: Production applications requiring top accuracy
OpenAI text-embedding-3-small
1536 dimensions, cost-effective
Best for: Budget-conscious applications
Cohere Embed v3
1024 dimensions, multilingual, compression
Best for: Multilingual semantic search
Sentence Transformers
Open-source, customizable, local deployment
Best for: Custom fine-tuning, privacy
Voyage AI
Domain-specific embeddings available
Best for: Legal, code, finance domains
MODEL COMPARISON
| MODEL | DIMS | COST |
|---|---|---|
| OpenAI Large | 3072 | $$$ |
| OpenAI Small | 1536 | $$ |
| Cohere v3 | 1024 | $$ |
| Sentence-T | 384-768 | Free |
WHY HIRE ME?
- ✓ Expert in all major embedding models
- ✓ Fine-tuning for domain-specific tasks
- ✓ Dimensionality optimization
- ✓ Cost-performance balancing
- ✓ NIT Jaipur AI/ML graduate
☎ CONTACT THE AUTHOR
udditalerts247@gmail.com
uddit.site
COMING NEXT ISSUE:
- › Fine-tuning embeddings
- › Multilingual strategies
- › Cost optimization
- › Performance benchmarks
★ NEED EMBEDDING EXPERTISE? LET'S OPTIMIZE YOUR AI ★