Comparing Semantic Search and Keyword Search

There are certain use cases where semantic search excels , at others keyword search excels. Hence the modern production systems use hybrid search strategies.

Aspect	Keyword Search	Semantic Search	Winner
Core Method	Literal word/phrase matching (inverted index, BM25)	Vector embeddings + meaning similarity	-
Strength	Precision & Exactness	Understanding & Relevance	Context dependent
Synonyms & Paraphrasing	Poor (unless manually expanded)	Excellent	Semantic
Handling Typos	Weak (needs fuzzy matching)	Good	Semantic
Context & Intent	Limited	Strong	Semantic
Speed & Efficiency	Very Fast & Lightweight	Slower & Resource Heavy	Keyword
Explainability	High (easy to see why a result matched)	Lower (black-box)	Keyword
Boolean Operators	Excellent (AND, OR, NOT, "exact phrase", etc.)	Weak	Keyword
Exact Matching	Excellent (IDs, codes, SKUs, error codes, citations)	Often poor	Keyword
Best For	Known-item search, technical logs, legal, compliance	Natural language, exploratory, conceptual search	Context dependent
Precision	High	Medium (can return loosely related results)	Keyword
Recall (finding related)	Lower	High	Semantic
Cost / Scalability	Cheaper & easier to scale	More expensive (embeddings + vector DB)	Keyword
Transparency & Debugging	Easy	Difficult	Keyword
New/Rare Terms	Reliable	Can struggle	Keyword