EmbeddingsHow AI Understands Meaning
Embeddings turn words into numbers that capture their meaning. They're the magic that lets AI understand that "dog" and "puppy" are related, search by meaning instead of keywords, and find similar content automatically.
What Are Embeddings?
What are embeddings?
Embeddings are how AI turns words and sentences into numbers. But not random numbers — numbers that capture meaning. Words with similar meanings get similar numbers, like "happy" and "joyful" getting nearby values while "sad" gets distant ones.
Why do we need them?
Computers only understand numbers, not words. Embeddings translate human language into a format AI can work with, while preserving what the words actually mean.
What's a "vector"?
A vector is just a list of numbers. An embedding for a word might be 1,536 numbers in a row. Each number represents some aspect of meaning. Together, they create a unique "fingerprint" for that word or sentence.
The simple version: Embeddings = "GPS coordinates for ideas." Every word, sentence, or document gets a position in meaning-space. Similar things are close together. AI uses this to understand, search, and compare.
Think of It Like...
Helpful ways to visualize embeddings
GPS Coordinates for Ideas
Just like GPS coordinates pinpoint locations on Earth, embeddings give "coordinates" to ideas in meaning-space. Similar ideas are close together.
Recipe Ingredients
A recipe has amounts of each ingredient. An embedding lists "amounts" of different meaning-ingredients (royalty: 0.8, furniture: 0.1, animal: 0.2).
DNA for Words
DNA sequences encode the traits of living things. Embeddings encode the "traits" of words — what they mean, how they're used, what they relate to.
How Embeddings Work
From text to numbers in 4 steps
Text Goes In
You feed a word, sentence, or entire document to an embedding model.
"The quick brown fox"
Model Processes It
The embedding model (trained on billions of texts) analyzes the meaning.
Neural network processing
Numbers Come Out
You get a vector — a long list of decimal numbers (typically 384 to 3,072 numbers).
[0.023, -0.142, 0.891, ...]
Meaning Is Encoded
Similar texts produce similar vectors. You can now compare, search, and group by meaning.
Similar ideas → nearby vectors
What Can You Do with Embeddings?
Real-world applications of this technology
Semantic Search
Search by meaning, not just keywords. "Dog photos" finds "pictures of puppies" even without the word "dog".
Example: Google Search, Perplexity
Recommendation Systems
Find similar items — movies, products, articles — based on content, not just user behavior.
Example: Netflix "More Like This"
RAG (Retrieval-Augmented Generation)
Find relevant documents to feed to ChatGPT so it can answer questions about your data.
Example: Chatbots for company docs
Clustering & Organization
Automatically group similar content — support tickets, feedback, emails — by topic.
Example: Zendesk ticket routing
Duplicate Detection
Find duplicate or near-duplicate content even when worded differently.
Example: Plagiarism checkers
Anomaly Detection
Spot unusual items that don't fit the normal pattern — fraud, errors, outliers.
Example: Security monitoring
Popular Embedding Models
Tools you can use to create embeddings
text-embedding-3-small
OpenAI • 1,536 dimensions
General purpose, cost-effectivetext-embedding-3-large
OpenAI • 3,072 dimensions
Higher accuracy, complex tasksvoyage-3
Voyage AI • 1,024 dimensions
Code, technical docsembed-v3
Cohere • 1,024 dimensions
Multilingual contentall-MiniLM-L6-v2
Sentence Transformers • 384 dimensions
Free, runs locallyWhere to Store Embeddings
Vector databases are built to search embeddings fast
Pinecone
Fully managed vector database. Easy to start, scales automatically.
Managed CloudWeaviate
Open-source with built-in ML models. Good for complex queries.
Open Source / CloudChroma
Lightweight, embeds in your app. Great for prototypes and small projects.
Open SourceQdrant
High-performance with filtering. Good for production workloads.
Open Source / Cloudpgvector
PostgreSQL extension. Use your existing Postgres database.
Open SourcePractical Tips
Advice for working with embeddings in real projects
Chunk your documents
Don't embed entire books. Split into paragraphs or sections (100-500 words) for better retrieval.
Use the right model
Smaller models are faster and cheaper. Only use large models when you need the accuracy.
Store metadata
Save the original text alongside vectors. You'll need it to show results to users.
Test similarity thresholds
A 0.8 similarity score might be great or terrible depending on your use case. Test and tune.
Key Terms
Vector
A list of numbers representing something. An embedding is a vector that represents meaning.
Dimensions
How many numbers in the vector. More dimensions = more detailed representation (but slower).
Similarity
How close two vectors are. Usually measured as cosine similarity (0 to 1, higher = more similar).
Vector Database
A database optimized for storing and searching vectors quickly.
Semantic Search
Search by meaning, not exact keywords. "Automobile" matches "car".
Nearest Neighbors
Finding vectors closest to your query — the most similar items.