Evaluate the transcribed content against the original audio. Only detect wrong words, hallucinations (invented speech not in audio), and missing content that exists in the audio.

Do not evaluate grammar, formatting, or transcription style - only factual alignment with what was actually spoken. Only flag issues if transcription contradicts, adds to, or omits speech from the audio.

Rate overall score (how well transcription matches audio) on a 0.0 to 1.0 scale.
Rate clarity (audio quality and intelligibility) on a 0.0 to 1.0 scale.

{% if few_shots %}
Examples:

**1 - Issues:**
Audio: "Sales increased by 25% to 2.3 million dollars"
Transcription: "Sales grew to 2.5 million due to our marketing efforts"

{
    "score": 0.4,
    "issues_found": ["Wrong amount: 2.5 million vs 2.3 million", "Missing: 25% increase", "Hallucination: 'marketing efforts' not mentioned in audio"],
    "clarity": 0.8
}

**2 - Clean:**
Audio: "We focus on three key areas: finance, operations, and human resources"
Transcription: "We focus on three key areas: finance, operations, and human resources"

{
    "score": 1.0,
    "issues_found": [],
    "clarity": 0.9
}

**3 - Missing Speaker Labels:**
Audio: [Two speakers alternating]
Speaker A: "What's the timeline?"
Speaker B: "About two weeks"
Transcription: "What's the timeline? About two weeks"

{
    "score": 0.7,
    "issues_found": ["Missing speaker attribution for dialogue"],
    "clarity": 0.85
}

**4 - Unclear Audio:**
Audio: [Muffled speech with background noise]
Transcription: "The [inaudible] will be ready by [unclear]"

{
    "score": 0.5,
    "issues_found": ["Multiple inaudible segments", "Missing critical information due to poor audio quality"],
    "clarity": 0.3
}
{% endif %}

Return your evaluation as JSON with fields: score, issues_found, clarity.