Interpret what a user meant before matching, through normalisation, intent detection, and entity extraction. Use when queries are short, ambiguous, or full of product-specific language.
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---
name: query-understanding
description: Interpret what a user meant before matching, through normalisation, intent detection, and entity extraction. Use when queries are short, ambiguous, or full of product-specific language.
---
# Query understanding
A query is a compressed intent. Two words may name a product, describe a
problem, or ask a question, and treating all three as bags of tokens
produces mediocre results for each. Understanding happens before
matching and shapes everything after.
## Method
1. **Normalise before anything else.** Trim, lowercase, unify
punctuation and unicode forms so equivalent queries take the same
path (see character-encoding).
2. **Detect the query type.** Navigational, informational, and
transactional queries want different result shapes, and misreading
the type is why a good match still feels wrong.
3. **Extract structure from the text.** A query containing a size, a
date, a brand, or a code is partly a filter, and pulling that out
beats hoping the ranker handles it (see faceted-search).
4. **Expand cautiously.** Adding synonyms and related terms improves
recall and dilutes precision, so expansion needs measuring rather
than assuming (see synonyms-and-stemming).
5. **Handle the head and the tail differently.** Frequent queries
deserve curated results; the long tail needs the general path to
degrade gracefully.
6. **Keep the original query visible and restorable.** Users must see
what was interpreted and be able to override it, since silent
rewriting is deeply frustrating when wrong.
7. **Log the interpretation, not just the query.** Debugging relevance
requires knowing what the system thought the user meant (see
search-analytics).
## Boundaries
- Understanding improves matching; it cannot rescue an index that lacks
the content being asked for.
- Aggressive rewriting produces confident wrong answers, which users
trust less than an honest miss.
- Language-specific handling is required, since tokenisation and
segmentation differ fundamentally between scripts.
