Semantic search
Search that matches the meaning of a query against the meaning of content, using vector similarity rather than keyword overlap.
In depth
What it really means
Semantic search compares meaning as geometry. Query and content both become vectors, and closeness in that space means relatedness. A page can match a query without sharing a single word with it.
This is why keyword density stopped mattering and topical coverage started. The system is measuring whether you actually cover the concept, and repeating a phrase does not move a vector toward the concept it names.
How it works
- Content is split into passages and embedded as vectors.
- Vectors are stored in an index. See vector database.
- The query is embedded using the same model.
- Similarity is computed, usually with cosine similarity, and the closest passages returned.
Pros & cons
Pros
- Genuinely useful content wins over keyword-optimized content more often.
- You match questions you never explicitly targeted.
- Synonyms and paraphrases work without any extra effort.
Cons
- Exact-match precision drops, which is why pure semantic search is rarely used alone.
- Vague, generalist writing embeds vaguely and matches nothing well.
- You cannot inspect why a passage did or did not match.
Common mistakes
- Still writing to a keyword density target.
- Covering a topic shallowly across many pages instead of properly on one.
- Hedging language that dilutes what a passage is actually about.
- Assuming semantic search replaced keyword matching. Most production systems run both.
Best practices
- Cover the concept and its adjacent concepts properly.
- Write in specific, concrete language. Vagueness embeds badly.
- Keep each section about one idea so its vector is sharp.
- Use natural synonyms rather than repeating an exact phrase.
- Build depth on a topic instead of breadth across unrelated ones.
FAQs
What is semantic search?
Search that matches meaning rather than keywords, by converting both query and content into vectors and measuring how close they are.
How does semantic search change content?
It rewards genuine, specific coverage of a concept. Repeating a keyword does not make your content mean more, so density targets no longer do anything.
Is keyword matching dead?
No. Most production systems run keyword and semantic retrieval together, which is called hybrid search, because each catches things the other misses.
How do I optimize for semantic search?
Write specifically and concretely about one idea per section, cover the adjacent concepts, and use natural language rather than repeating an exact phrase.
Keep reading
Related on LymLyt
Beyond LymLyt
Further reading
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