Type "why hasn't my new website address started working" into a classic search box and you may get pages about website builders. The page you needed, about DNS propagation, never uses the words you typed. That gap is the difference between keyword search and semantic search.
This guide explains both approaches in plain language, shows where each one wins, and describes how knowledge platforms like FaqVault combine semantic search with verified answers.
How keyword search works
Keyword search breaks documents into words and stores them in an inverted index, which maps each word to the documents that contain it. A query is split into words, matching documents are retrieved, and a scoring function ranks them by how often and where the words appear.
It is fast, predictable and excellent at exact matches. Its weakness is vocabulary: if you and the author use different words for the same idea, the index has no way to connect them.
How semantic search works
Semantic search matches meaning instead of words. A language model converts each document and each query into an embedding: a list of numbers that places the text in a high-dimensional space where similar meanings sit close together. Google's Machine Learning Crash Course on embeddings is a good primer on the idea.
At query time, the engine embeds your question and retrieves the documents whose vectors are nearest. Because "new website address not working" and "DNS propagation delay" land near each other in that space, the right answer surfaces even with zero words in common.
Side by side
The same question, answered two ways:
- Query: "can I bring my phone charger on a plane": keyword search looks for "phone", "charger", "plane"; semantic search also finds entries about power banks, lithium batteries and carry-on rules.
- Query: "my laptop won't wake up": keyword search needs "wake"; semantic search matches sleep-mode, hibernate and black-screen troubleshooting.
- Query: "ERR_CERT_DATE_INVALID": here keyword search wins, because the exact error code is the most important signal.
Where keyword search still wins, and why hybrid is common
Exact identifiers such as error codes, product SKUs, function names and legal citations are best matched literally. Semantic models can blur them into "something similar". That is why many production systems use hybrid search: semantic retrieval for intent, keyword matching for precision, and a ranking step that blends the two.
Retrieval is not truth: why verification matters
Semantic search is very good at finding text that is similar to your question. It says nothing about whether that text is correct. An engine can confidently retrieve a well-written but outdated answer.
That is the problem FaqVault was built to solve. Every entry goes through an AI-assisted verification and curation step before it enters the vault, so semantic search is only ever choosing between answers that have already been checked.
How to ask better questions of any semantic search engine
- Ask the full question in natural language. Context helps the model.
- Include the situation ("on a Mac", "in the EU", "for a Next.js app").
- Keep exact error messages or codes intact, in quotes if possible.
- Browse the category the top answer belongs to for related questions.
Try it
FaqVault's vault holds thousands of verified answers across more than thirty categories, from Programming to Internet Infrastructure and Travel. It is free, with no account and no paywall.
Frequently asked questions
What is semantic search in simple terms?
Semantic search finds results by meaning rather than exact words, using AI embeddings that place similar ideas close together.
Is semantic search better than keyword search?
For natural-language questions, usually yes. For exact codes, names or identifiers, keyword search is often more precise. Many systems combine both.
What are embeddings?
Embeddings are numerical representations of text produced by a model, arranged so that texts with similar meaning have similar numbers.