Does high vector similarity mean a document contains the true answer?
Answer out loud or in your head first, then compare.
Answer
No. Similarity helps find topically related passages. It doesn't by itself check that they're current, true and relevant to the question. The sentences “this operation can be performed” and “this operation cannot be performed” are about the same topic, even though they mean the opposite.
So after finding candidates, you have to judge whether they actually support the answer. It's often worth searching exact identifiers by text as well. It's important to use compatible representations for the question and the documents; I don't accidentally mix vectors from different models. A two-dimensional similarity chart is also just a simplified view that can distort distances.
Why it matters: Takes the basics of embeddings to the level of their limits.
Possible follow-ups: What do you check if search finds the topic but not the right rule?
How did it go? Be honest — “Not yet” puts the question on your review list.
Start with
- F-P01 · What is an LLM? — LLM fundamentals and AI engineering
- F-P03 · What are a token and a context window? — LLM fundamentals and AI engineering
Further reading
- Claude Platform: Embeddings(opens in a new tab)Docs; accessed 22.10.2026. Semantic representations and how to choose an embedding model.