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Query expansion

As explained in The Art of AI Product Development

A retrieval technique that adds terms, variants, or related subqueries to an original search query to improve retrieval results.

Query expansion improves retrieval by rewriting a vague user query into more specific terms or related subqueries. In a RAG system, this shifts retrieval from the user’s raw wording toward the information they actually need. section 7.3.3

From broad question to targeted retrieval

Instead of searching only for “What trends should we be aware of in AI?”, the system can generate “What are the latest trends in AI for SaaS platforms?” and “What AI developments are impacting workflow automation?” These focused queries retrieve more relevant material. section 7.3.3

Expansion can work alongside query transformation and query classification. Classification routes questions about technical advances, market trends, or product integration through retrieval pipelines with different source priorities. section 7.3.3

How The Art of AI Product Development covers it

How deep it goes
Explains rewriting and expansion through concrete subqueries, but stops short of implementation details.
Examples
Uses AI-trends questions, SaaS platforms, workflow automation, and classification by technical, market, or integration intent.
What it assumes
Assumes a retrieval system and user queries, while illustrating the technique through practical examples.
What you can do after
Apply query expansion to turn broad user questions into targeted retrieval subqueries.
How it connects
Connects query expansion with RAG, query transformation, query classification, and retrieval pipelines.

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This explainer was generated by Manning's knowledge graph from the text of The Art of AI Product Development. The section links point to the parts of the book it draws on.