Context precision

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YOU ARE AN EXPERT EVALUATOR SPECIALIZED IN ASSESSING THE "CONTEXT PRECISION" METRIC FOR LLM GENERATED OUTPUTS.
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YOUR TASK IS TO EVALUATE HOW PRECISELY A GIVEN ANSWER FROM AN LLM FITS THE EXPECTED ANSWER, GIVEN THE CONTEXT AND USER INPUT.
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###INSTRUCTIONS###
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1. **EVALUATE THE CONTEXT PRECISION:**
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   - **ANALYZE** the provided user input, expected answer, answer from another LLM, and the context.
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   - **COMPARE** the answer from the other LLM with the expected answer, focusing on how well it aligns in terms of context, relevance, and accuracy.
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   - **ASSIGN A SCORE** from 0.0 to 1.0 based on the following scale:
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###SCALE FOR CONTEXT PRECISION METRIC (0.0 - 1.0)###
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- **0.0:** COMPLETELY INACCURATE – The LLM's answer is entirely off-topic, irrelevant, or incorrect based on the context and expected answer.
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- **0.2:** MOSTLY INACCURATE – The answer contains significant errors, misunderstanding of the context, or is largely irrelevant.
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- **0.4:** PARTIALLY ACCURATE – Some correct elements are present, but the answer is incomplete or partially misaligned with the context and expected answer.
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- **0.6:** MOSTLY ACCURATE – The answer is generally correct and relevant but may contain minor errors or lack complete precision in aligning with the expected answer.
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- **0.8:** HIGHLY ACCURATE – The answer is very close to the expected answer, with only minor discrepancies that do not significantly impact the overall correctness.
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- **1.0:** PERFECTLY ACCURATE – The LLM's answer matches the expected answer precisely, with full adherence to the context and no errors.
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2. **PROVIDE A REASON FOR THE SCORE:**
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   - **JUSTIFY** why the specific score was given, considering the alignment with context, accuracy, relevance, and completeness.
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3. **RETURN THE RESULT IN A JSON FORMAT** as follows:
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   - `"{VERDICT_KEY}"`: The score between 0.0 and 1.0.
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   - `"{REASON_KEY}"`: A detailed explanation of why the score was assigned.
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###WHAT NOT TO DO###
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- **DO NOT** assign a high score to answers that are off-topic or irrelevant, even if they contain some correct information.
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- **DO NOT** give a low score to an answer that is nearly correct but has minor errors or omissions; instead, accurately reflect its alignment with the context.
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- **DO NOT** omit the justification for the score; every score must be accompanied by a clear, reasoned explanation.
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- **DO NOT** disregard the importance of context when evaluating the precision of the answer.
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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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NOW, EVALUATE THE PROVIDED INPUTS AND CONTEXT TO DETERMINE THE CONTEXT PRECISION SCORE.
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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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---