How it works
Semantic search is a way to retrieve information based on the likely meaning of a query, not just the exact presence of the same words. In general, the system transforms queries and documents into numerical representations called embeddings and compares the representations to find related items.
The process usually has two steps. First, documents are prepared, split when necessary and indexed. Then, a query is converted into a compatible representation and compared with the stored items. The search can return the most similar passages, which a person or another component uses to answer the question.
Similarity is not synonymous with truth. A passage can be close to the query and still not answer what was asked. The system can also fail to find an important document if the indexing, metadata or representation are inadequate.
Practical example
A person searches the help center for "I can't log into my account". The manual might use the expression "access recovery". An exact keyword search might not find the article, but semantic search can bring the two phrases closer because they deal with a similar problem.
The team can compare results with real queries and check if the correct article appears among the first results. It should also check if the search differentiates close cases, such as resetting a password and unlocking an account. Filters by product, language or customer type can limit the search to appropriate documents.
If the search feeds an assistant, the system can send the retrieved passages together with the question. The answer should remain linked to the sources used, so the person can check the content. Updated documents need to be reindexed, and access permissions must be respected during retrieval.
What it's for
Semantic search is useful in knowledge bases, catalogs, support, internal research and retrieval systems for AI. It helps when people formulate queries with different words than those used in the documents.
In many products, semantic search is combined with lexical search, filters and business rules. Lexical search finds word or phrase matches; semantic search can locate meaning relationships. A hybrid approach can leverage both, but it should be evaluated with examples and own criteria.
To measure quality, gather representative questions and indicate which documents should be found. Observe relevance, coverage, irrelevant results and behavior for queries with no answer. Do not evaluate only a demo with a few questions: different areas, languages and writing styles can reveal different problems.
Limits and maintenance
Embeddings can reflect ambiguities, outdated data or language differences between the query and the documents. Metadata helps restrict results, but needs to be consistent. If the content changes, the indexing must also be updated.
Search should not reveal material that the person is not permitted to see. Apply authorization before presenting passages and test the system with queries that try to bypass filters. For important answers, keep references to the source and offer a path for human review.