Query Intent Refinement

User queries often don't match document phrasing, creating retrieval challenges that can be addressed through query transformation:

Pseudo-Relevance Feedback (PRF):

• Performs initial retrieval to find potentially relevant documents
• Extracts key terms from these documents to expand the original query
• Creates a more comprehensive query that matches relevant document terminology

Neural Query Expansion:

• Uses LLMs to generate alternative phrasings of the original query
• Creates multiple search queries from a single user question
• Improves recall by covering different ways information might be expressed

Hypothetical Document Content (HyDE):

• Uses an LLM to generate an ideal answer document
• Retrieves real documents similar to this hypothetical document
• Bridges the query-document vocabulary gap effectively

These techniques transform user questions into more effective retrieval queries, significantly improving the ability to find relevant information even when expressed differently.