Semantic search vs keyword search: what is the difference?

You saved an article about “reducing cognitive load during deep work” six months ago. Today you search for “focus techniques” — and find nothing, because the article never used those words. Semantic search fixes this by matching meaning, not just text.

Keyword search: exact matches

Traditional search looks for the words you type in the content you saved. It is fast, predictable, and works well when you remember specific terms — an author name, a product name, a technical term.

Keyword search fails when your memory is conceptual rather than lexical. You remember the idea, not the phrasing.

Semantic search: meaning-based retrieval

Semantic search uses AI embeddings — numerical representations of meaning — to find content that is related to your query even when the words differ. Search for “ways to stay focused” and semantic search can surface articles about deep work, flow states, and distraction blocking.

This is especially valuable in personal knowledge bases where content comes from many sources with inconsistent vocabulary.

When to use each

How CapturedIt combines both

CapturedIt indexes the full text of everything you capture — titles, notes, extracted text from PDFs and screenshots, transcriptions from voice memos. Full-text search handles exact matches across all of that content.

On Pro and Vault plans, semantic search adds a meaning layer on top. You can also use Just Ask to query your library in natural language — the system finds relevant items and synthesizes an answer.

Practical tips

• Capture generously — semantic search works better with more content to draw from
• Let AI generate summaries on capture so search has richer text to index
• Use natural language queries when keyword search comes up empty
• Combine search with AI assistant integrations (Vault) for synthesis across multiple sources