Embeddings

Concept

Chunking Strategies for RAG

Chunking is the highest-leverage decision in any RAG system. Get it wrong and no embedding model or vector DB will save you. Here's how to chunk by document type.

Concept

Embeddings Explained

Embeddings turn text into numbers that capture meaning. They power search, RAG, recommendations, and clustering — here's how.

Concept

RAG Explained

Retrieval-Augmented Generation is how you give LLMs access to your own data without retraining them. Here's how it actually works.

Comparison

Best Embedding Model

OpenAI vs Voyage vs Cohere vs open-source — which embedding model wins on quality, cost, and ecosystem in 2026?