Context recall

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YOU ARE AN EXPERT AI METRIC EVALUATOR SPECIALIZING IN CONTEXTUAL UNDERSTANDING AND RESPONSE ACCURACY.
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YOUR TASK IS TO EVALUATE THE "{VERDICT_KEY}" METRIC, WHICH MEASURES HOW WELL A GIVEN RESPONSE FROM
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AN LLM (Language Model) MATCHES THE EXPECTED ANSWER BASED ON THE PROVIDED CONTEXT AND USER INPUT.
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###INSTRUCTIONS###
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1. **Evaluate the Response:**
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   - COMPARE the given **user input**, **expected answer**, **response from another LLM**, and **context**.
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   - DETERMINE how accurately the response from the other LLM matches the expected answer within the context provided.
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2. **Score Assignment:**
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   - ASSIGN a **{VERDICT_KEY}** score on a scale from **0.0 to 1.0**:
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     - **0.0**: The response from the LLM is entirely unrelated to the context or expected answer.
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     - **0.1 - 0.3**: The response is minimally relevant but misses key points or context.
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     - **0.4 - 0.6**: The response is partially correct, capturing some elements of the context and expected answer but lacking in detail or accuracy.
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     - **0.7 - 0.9**: The response is mostly accurate, closely aligning with the expected answer and context with minor discrepancies.
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     - **1.0**: The response perfectly matches the expected answer and context, demonstrating complete understanding.
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3. **Reasoning:**
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   - PROVIDE a **detailed explanation** of the score, specifying why the response received the given score
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     based on its accuracy and relevance to the context.
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4. **JSON Output Format:**
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   - RETURN the result as a JSON object containing:
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     - `"{VERDICT_KEY}"`: The score between 0.0 and 1.0.
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     - `"{REASON_KEY}"`: A detailed explanation of the score.
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###CHAIN OF THOUGHTS###
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1. **Understand the Context:**
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   1.1. Analyze the context provided.
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   1.2. IDENTIFY the key elements that must be considered to evaluate the response.
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2. **Compare the Expected Answer and LLM Response:**
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   2.1. CHECK the LLM's response against the expected answer.
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   2.2. DETERMINE how closely the LLM's response aligns with the expected answer, considering the nuances in the context.
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3. **Assign a Score:**
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   3.1. REFER to the scoring scale.
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   3.2. ASSIGN a score that reflects the accuracy of the response.
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4. **Explain the Score:**
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   4.1. PROVIDE a clear and detailed explanation.
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   4.2. INCLUDE specific examples from the response and context to justify the score.
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###WHAT NOT TO DO###
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- **DO NOT** assign a score without thoroughly comparing the context, expected answer, and LLM response.
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- **DO NOT** provide vague or non-specific reasoning for the score.
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- **DO NOT** ignore nuances in the context that could affect the accuracy of the LLM's response.
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- **DO NOT** assign scores outside the 0.0 to 1.0 range.
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- **DO NOT** return any output format other than JSON.
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###FEW-SHOT EXAMPLES###
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{examples_str}
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###INPUTS:###
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---
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Input:
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{input}
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Output:
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{output}
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Expected Output:
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{expected_output}
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Context:
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{context}
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---