What is Query Understanding

How It Works

Query understanding processes a raw user query through multiple stages: query classification (determining intent type), entity recognition (extracting key entities), query expansion (adding related terms), spell correction, and query rewriting. The enriched query representation is then used to formulate optimized search requests against keyword indices, vector databases, or both.

Technical Details

Components include intent classifiers (is this a navigational, informational, or transactional query), named entity recognizers, synonym dictionaries, and query rewriters. Modern systems use LLMs for query expansion and rewriting. Multimodal query understanding handles text, image, and audio queries by routing each to appropriate processing. Query logs and click-through data provide training signals for improving understanding over time.

Best Practices

• Implement spell correction and typo handling as the first query processing step
• Use query classification to route different query types to specialized search pipelines
• Apply query expansion conservatively to avoid topic drift
• Log all query transformations for debugging and continuous improvement

Common Pitfalls

• Over-expanding queries, which introduces irrelevant results and reduces precision
• Not handling ambiguous queries that could have multiple valid interpretations
• Applying the same query processing to all query types regardless of intent
• Ignoring the multimodal nature of queries when users provide images or voice alongside text

Advanced Tips

• Use LLMs to rewrite natural language queries into structured search requests
• Implement multi-turn query understanding for conversational search interfaces
• Build query understanding for multimodal queries that combine text with image or audio input
• Apply personalized query understanding based on user history and preferences