Query
A request sent to an AI system or database to retrieve information or generate a response. In RAG, queries trigger retrieval from the knowledge base.
In-Depth Explanation
In AI systems, a query is a request for information or action. The term applies both to user requests to AI systems and to the search operations performed against knowledge bases in RAG architectures.
Query types in AI:
- Natural language queries: User questions in plain language
- Embedding queries: Vector representations for similarity search
- Structured queries: Formal database queries (SQL, etc.)
- Hybrid queries: Combining semantic and keyword search
Query processing stages:
- Query understanding: Parsing user intent
- Query expansion: Adding synonyms or related terms
- Query transformation: Converting to searchable form
- Retrieval: Finding relevant results
- Ranking: Ordering by relevance
- Response generation: Producing the final answer
Query optimization techniques:
- Query rewriting (improving clarity)
- Query decomposition (breaking complex queries into parts)
- HyDE (hypothetical document embedding)
- Query routing (directing to appropriate sources)
Business Context
Query design affects both response quality and costs for US businesses. Well-structured queries improve relevance and reduce token usage, critical for American companies managing AI budgets.
How Clever Ops Uses This
We implement sophisticated query processing for American businesses, including query expansion, routing, and optimization to ensure US users get accurate, contextually relevant answers.
Example Use Case
"A customer asking "What's your return policy?" triggers a query to find relevant documentation, which is then used to generate an accurate response."