AI & LLM Index
Glossary · Retrieval and RAG

What is Retrieval latency?

Retrieval latency is a retrieval or retrieval-augmented generation concept. It is part of the vocabulary teams use when they compare models, design prompts or review an AI system.

Definition

Retrieval latency is a retrieval or retrieval-augmented generation concept. It is part of the vocabulary teams use when they compare models, design prompts or review an AI system. The term is used in retrieval and rag discussions, where precision matters: two teams can use the same word to mean slightly different things. In this glossary, retrieval latency is treated as a practical concept rather than a formal specification.

Why it matters

Retrieval terms describe how external documents are indexed, searched and inserted into a prompt. They matter because most production systems rely on fresh or private data that is not in the model's weights.

Example

Suppose you are comparing Firefunction Reasoning with another model for a production task. Retrieval latency is one of the factors behind the numbers you see: Firefunction Reasoning lists a 8,192-token context window, costs $9.08 per million input tokens and scores 81.1 on IFEval. Understanding the term helps you decide which of those differences actually matter for your workload.

How to evaluate it

  • Check whether retrieval latency is defined the same way in the documentation you are reading.
  • Ask which measurement or trade-off the term is describing before comparing numbers.
  • Separate vendor claims from independently observed behaviour on your own data.
  • Write down the definition your team uses so reviews and evaluations stay consistent.

Related terms

Retrieval-augmented generationRetrieval and RAG RAGRetrieval and RAG EmbeddingRetrieval and RAG Vector databaseRetrieval and RAG Semantic searchRetrieval and RAG Hybrid searchRetrieval and RAG

Related guides

How to cut latency in an LLM appHow-to How to evaluate retrieval qualityHow-to

FAQ

What does Retrieval latency mean?

Retrieval latency is a retrieval or retrieval-augmented generation concept. It is part of the vocabulary teams use when they compare models, design prompts or review an AI system.

Why does Retrieval latency matter?

Retrieval terms describe how external documents are indexed, searched and inserted into a prompt. They matter because most production systems rely on fresh or private data that is not in the model's weights.

How is Retrieval latency used in practice?

Suppose you are comparing Firefunction Reasoning with another model for a production task. Retrieval latency is one of the factors behind the numbers you see: Firefunction Reasoning lists a 8,192-token context window, costs $9.08 per million input tokens and scores 81.1 on IFEval. Understanding the term helps you decide which of those differences actually matter for your workload.