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Contextual Recall

Contextual Recall is a single-turn metric used to evaluate a RAG retriever

Overview

The contextual recall metric is a single-turn RAG metric that uses LLM-as-a-judge to assess whether your retriever has surfaced enough relevant context to produce an answer similar to the expected output.

Required Parameters

These are the parameters you must supply in your test case to run evaluations for contextual recall metric:

inputstringRequired

The input query you supply to your RAG application.

expected_outputstringRequired

The expected output your RAG application has to generate for a given input.

retrieval_contextlist of stringRequired

The retrieved context your retriever outputs for a given input sorted by their rank.

How Is It Calculated?

The contextual recall metric first extracts distinct statements from the expected output using an LLM, then uses the same LLM to check how many of those statements are supported by the retrieved context nodes.


Contextual Recall=Number of Attributable StatementsTotal Number of Statements\text{Contextual Recall} = \frac{\text{Number of Attributable Statements}}{\text{Total Number of Statements}}

The final score is the proportion of attributable statements in expected output.

Usage

To run the contextual recall metric on Confident AI, add it to a single-turn metric collection. This will allow you to use contextual recall metric for:

  • Single-turn E2E testing
  • Single-turn component-level testing
  • Online and offline evals for traces and spans

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