Embeddings Explained: The Technology Behind AI Search, Recommendations, and Memory
TL;DR
Embeddings are how AI models represent meaning as numbers. Understanding embeddings — not just vector databases — is what separates PMs who can architect intelligent search, recommendations, and memory systems from those who can't. This guide explains what embeddings are, how they're generated, and how to use them to build better AI products.
What Are Embeddings?
An embedding is a list of numbers — a vector — that represents the meaning of some content. A sentence, a product description, an image, or a user's interaction history can all be converted into an embedding.
The key property
Semantically similar things have numerically similar embeddings. “I need help with my account” and “I can't log in” will have embeddings close together in vector space, even though they share no words. This property makes embeddings the foundation of modern semantic search, recommendations, clustering, and AI memory systems.
Leading embedding models in 2026:
OpenAI text-embedding-3-large
3072 dimensions, strong general performance, widely used
OpenAI text-embedding-3-small
1536 dimensions, cheaper, good for high-volume use cases
Cohere Embed v3
Strong multilingual performance, good for enterprise
Voyage AI
Strong domain-specific models (finance, law, code)
Google text-embedding-004
Tight Gemini ecosystem integration
all-MiniLM-L6-v2
Open-source, 384 dims, fast, self-hostable
The Embedding Pipeline: End to End
Step 1: Chunking
Before embedding documents, split them into chunks. Sweet spot: 200–500 tokens with 10–20% overlap. Too small loses context, too large makes retrieval imprecise.
Step 2: Embedding Generation
Each chunk is passed through the embedding model → you get a vector. This is done once at index time, not at query time (which is why RAG search is fast).
Step 3: Storage
Vectors are stored in a vector database (Pinecone, Weaviate, Qdrant, pgvector) alongside the original text and metadata.
Step 4: Query Embedding
At query time, the user's query is embedded using the same model. Critical: if you switch embedding models, all stored embeddings become incompatible.
Step 5: Similarity Search
The query vector is compared against stored vectors. The most similar vectors and their associated content are returned.
Beyond Search: Advanced Embedding Use Cases
Semantic clustering for user research
Embed thousands of user feedback items → cluster by similarity → each cluster represents a distinct theme. Replaces hours of manual tagging.
Anomaly detection
Embed normal usage patterns → flag inputs unusually far from the cluster → catch edge cases, abuse, and out-of-distribution requests.
Duplicate detection
Embed support tickets or bug reports → find near-duplicates with high cosine similarity → deduplicate before routing to teams.
Recommendation systems
Embed user behavior → find users with similar behavior embeddings → recommend content that similar users engaged with.
Semantic caching
Cache LLM responses by query embedding. When new query is semantically similar (cosine > 0.95), return cached response. Cuts costs 20–40%.
Memory for AI agents
Store user interactions as embeddings → retrieve most relevant past interactions at query time → inject into context for long-term agent memory.
Apply These Concepts in the AI PM Masterclass
You'll design complete RAG and memory systems for real products — live, with a Salesforce Sr. Director PM.
Common Embedding Mistakes
Using the wrong chunk size
The #1 cause of poor RAG performance. Test retrieval quality with multiple chunk sizes before optimizing anything else.
Not filtering before search
Combine metadata filtering with vector search. Date and tag filters should happen before similarity ranking, not after.
Embedding too much noise
Embedding navigation menus, headers, and boilerplate alongside real content degrades search quality. Pre-process first.
Ignoring embedding model versioning
If you switch embedding models, all stored embeddings become incompatible. Build migration tooling before you need it.
Skipping evaluation
Build a retrieval evaluation set: 20–50 queries with known correct retrievals. Measure precision@k before and after any changes.
Reranking: The Layer Most Teams Skip
Vector search returns the most semantically similar chunks. But similarity isn't always relevance. A reranker rescores the top-K results for actual relevance to the specific query.
The reranking pattern:
Rerankers consistently improve RAG quality by 10–25% on complex queries. Leading options: Cohere Rerank, Voyage Rerank, cross-encoders from HuggingFace. The PM trade-off: reranking adds 100–400ms latency and cost per query.
Master Embeddings & Retrieval Architecture
Embeddings and retrieval architecture are core topics in the AI PM Masterclass. You'll design complete RAG and memory systems for real products.