How they work, without formulas
Picture a map where every sentence occupies a point. Sentences about the same thing sit close together, even if they use different words; those about something else sit far apart. An embedding model is what assigns each sentence its point. The map has hundreds or thousands of dimensions instead of two, but the idea is the same.
When you ask a question, the question becomes a point too, and the system picks the nearest chunks. That is how retrieval works in a RAG.
Why they’re useful in marketing
- Finding things without remembering the words. Reports, research and notes become searchable by topic.
- Different languages. Multilingual models bring together texts with the same meaning in different languages.
- Grouping. Similar conversations, reviews or requests can be clustered by theme.
- Connecting. Finding material related to what you’re reading.
Where they reach their limits
- Close doesn’t mean correct. Two sentences can be similar yet say opposite things: “price is the strong point” and “price is the weak point”.
- Numbers and proper names. An exact figure or code is better found with keyword search.
- Connections. Similarity doesn’t link information spread across different documents: for that you need a graph.
- Chunk quality. Chunks that are too long mix topics; chunks that are too short lose context.
