How do I improve query results?

Techniques include: query expansion (add synonyms), query rewriting (clarify intent), HyDE (generate hypothetical answer to search), and routing (direct to appropriate knowledge sources).

What is query decomposition?

Breaking complex queries into simpler sub-queries that can be answered independently, then combining results. "Compare products A and B on price and features" becomes separate queries for each product's details.

How does semantic query work?

The query is converted to an embedding vector, then compared against document embeddings using similarity metrics. This finds conceptually related content even without matching keywords.

Hypothetical Document Embedding. The LLM generates a hypothetical answer, which is then embedded and used for retrieval. This often retrieves more relevant documents than the original query.

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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 optimisation 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. Well-structured queries improve relevance and reduce token usage.

How Clever Ops Uses This

We implement sophisticated query processing for Australian businesses, including query expansion, routing, and optimisation to ensure users get accurate 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."

Frequently Asked Questions

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