Retrieval-augmented generation (RAG)

Why RAG is the marketer's opening

A pure language model answers from frozen training memory — you cannot influence it between training runs. A RAG system answers from what it retrieves now. Every grounded answer is therefore a live competition: whose passages get pulled, whose facts get used, whose links get shown. GEO exists because RAG exists.

The mechanics that matter

• Chunking. Pages are split into passages of roughly 100–500 tokens before matching. Each chunk competes alone. A brilliant insight buried mid-paragraph competes worse than the same insight stated cleanly under its own heading.
• Embedding match. Chunks are matched to queries by semantic similarity, not keyword overlap. Cover the meaning space — synonyms, rephrasings, the buyer's vocabulary, not yours.
• Grounding constraint. The model is instructed to answer from retrieved material. Confident, specific, well-sourced chunks give the model something safe to say; vague chunks push it toward competitors' specifics.

Design implications

1. One idea per section, stated in the first sentence.
2. Headings that match question shapes ("How much does X cost", "X vs Y").
3. Numbers and named entities in every chunk — "reduces setup time by 40%" outperforms "saves significant time".
4. Self-contained passages: if a chunk were quoted alone on another site, would it still make sense and still credit you?