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Technical Foundations
RAG & Embeddings
How Models Store and Retrieve Meaning
Retrieval-Augmented Generation (RAG) and embeddings define how modern AI systems understand, store, and retrieve meaning. Even if you never build a RAG system, understanding these concepts helps you structure content the way LLMs prefer.
What Embeddings Are
Embeddings are numerical representations of meaning. Each semantic block becomes a vector capturing:
Semantics
Relationships
Intent
Context
Better structure produces cleaner embeddings.
What RAG Does
RAG retrieves relevant embeddings and feeds them into the model before generation. This improves:
Accuracy
Relevance
Context
Factual grounding
Why This Matters
LLMs treat your pages as a collection of vectorized meaning units. This primer clarifies:
- Why semantic blocks work
- Why clarity improves retrieval
- Why ambiguity weakens embeddings