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50	Assess RAG retrieval quality by evaluating context relevance, precision, and usefulness for answering queries.

Context relevance

Measures what fraction of retrieved context is minimally needed to answer the query.

Use when: You want to check if your retrieval is returning too much irrelevant content.

How it works: Extracts only the sentences absolutely required to answer the query. Score = required sentences / total sentences.

:::warning This metric finds the MINIMUM needed, not all relevant content. A low score might mean good retrieval (found answer plus supporting context) or bad retrieval (lots of irrelevant content). :::

Example:

Query: "What is the capital of France?"
Context: "Paris is the capital. France has great wine. The Eiffel Tower is in Paris."
Score: 0.33 (only first sentence required)

Configuration

assert:
  - type: context-relevance
    threshold: 0.3 # At least 30% should be essential

Required fields

• query - User's question (in test vars)
• context - Retrieved text (in vars or via contextTransform)
• threshold - Minimum score 0-1 (default: 0)

Full example

tests:
  - vars:
      query: 'What is the capital of France?'
      context: 'Paris is the capital of France.'
    assert:
      - type: context-relevance
        threshold: 0.8 # Most content should be essential

Array context

Context can be provided as an array of chunks:

tests:
  - vars:
      query: 'What are the benefits of RAG systems?'
      context:
        - 'RAG systems improve factual accuracy by incorporating external knowledge sources.'
        - 'They reduce hallucinations in large language models through grounded responses.'
        - 'RAG enables up-to-date information retrieval beyond training data cutoffs.'
        - 'The weather forecast shows rain this weekend.' # irrelevant chunk
    assert:
      - type: context-relevance
        threshold: 0.5 # Score: 3/4 = 0.75

Dynamic context extraction

For RAG systems that return context with their response:

# Provider returns { answer: "...", context: "..." }
assert:
  - type: context-relevance
    contextTransform: 'output.context' # Extract context field
    threshold: 0.3

contextTransform can also return an array:

assert:
  - type: context-relevance
    contextTransform: 'output.chunks' # Extract chunks array
    threshold: 0.5

Score interpretation

• 0.8-1.0: Almost all content is essential (very focused or minimal retrieval)
• 0.3-0.7: Mixed essential and supporting content (often ideal)
• 0.0-0.3: Mostly non-essential content (may indicate poor retrieval)

Limitations

• Only identifies minimum sufficient content
• A single-paragraph (prose) context string is split into sentences on ./!/? boundaries; a context with two or more non-empty lines, or an array of chunks, is treated as already segmented and split by line/chunk. Sentence splitting is a lightweight heuristic that does not handle every abbreviation or decimal edge case — provide an array of chunks for the most precise denominator.
• Score interpretation varies by use case

Related metrics

• context-faithfulness - Does output stay faithful to context?
• context-recall - Does context support expected answer?

Further reading

• Defining context in test cases
• RAG Evaluation Guide