Feedback: guides-phone-call-segmentation

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/phone-call-segmentation
Category: guides
Generated: 05/08/2025, 4:38:50 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:38:49 pm

Technical Documentation Analysis & Feedback

Overall Assessment

This documentation provides a solid foundation but needs significant improvements in structure, completeness, and user guidance. Here’s my detailed feedback:

Critical Missing Information

1. Prerequisites & Requirements

• Missing: Python version requirements
• Missing: Expected audio format specifications (supported formats, file size limits, duration limits)
• Missing: Clear explanation of LeMUR credit costs and consumption rates
• Add: Minimum account tier requirements beyond “upgrade with credit card”

2. Error Handling & Troubleshooting

# Add this essential error handling section:
try:
    transcript = transcriber.transcribe(audio_url)
    if transcript.status == aai.TranscriptStatus.error:
        print(f"Transcription failed: {transcript.error}")
        return
except Exception as e:
    print(f"Error during transcription: {e}")
    return

3. Input Validation

• Missing: How to validate audio files before processing
• Missing: Handling of different audio formats and quality levels
• Missing: Maximum file size and duration limits

Structure & Organization Issues

1. Redundant Code Sections

The “Quickstart” and “Step-by-Step Instructions” contain nearly identical code. Recommendation:

• Keep Quickstart as a complete, copy-paste example
• Make Step-by-Step focus on explanations and variations
• Add a “Complete Example” section at the end

2. Logical Flow Problems

## Suggested New Structure:
1. Overview & Use Cases
2. Prerequisites & Setup
3. Quick Start (complete example)
4. Detailed Walkthrough
5. Customization Options
6. Output Processing
7. Error Handling
8. Best Practices
9. Troubleshooting

Content Clarity Issues

1. Insufficient Explanations

Current: “Define the phases you want to identify” Improved:

### Defining Call Phases
Phases represent distinct stages in your phone call. Choose phases that match your specific use case:

**Customer Support Example:**
- Introduction: Greeting and problem identification
- Investigation: Gathering details and troubleshooting
- Resolution: Providing solutions or next steps
- Closure: Confirming satisfaction and ending call

**Sales Call Example:**
- Opening: Introductions and rapport building
- Discovery: Understanding customer needs
- Presentation: Showcasing products/services
- Close: Handling objections and finalizing

2. Technical Details Missing

• Add: Explanation of why VTT format is used
• Add: Alternative input formats (plain text, SRT, etc.)
• Add: Token consumption estimation
• Add: Processing time expectations

Examples & Use Cases

1. Limited Use Case Coverage

Add these examples:

# Sales call phases
sales_phases = [
    "Opening & Rapport",
    "Needs Discovery",
    "Product Presentation",
    "Objection Handling",
    "Closing"
]

# Medical consultation phases
medical_phases = [
    "Patient Check-in",
    "Symptom Discussion",
    "Examination Planning",
    "Treatment Recommendation",
    "Follow-up Scheduling"
]

2. Output Processing Examples

Add this critical section:

import json

# Parse and work with the results
response_data = json.loads(result.response)

# Extract specific phase information
for phase in response_data["phases"]:
    duration = phase["end_time"] - phase["start_time"]
    print(f"{phase['name']}: {duration:.1f} seconds")
    print(f"Summary: {phase['summary']}\n")

# Find longest phase
longest_phase = max(response_data["phases"],
                   key=lambda x: x["end_time"] - x["start_time"])
print(f"Longest phase: {longest_phase['name']}")

User Experience Pain Points

1. No Validation or Feedback

Add:

# Validate API key
if not aai.settings.api_key or aai.settings.api_key == "YOUR_API_KEY":
    raise ValueError("Please set your AssemblyAI API key")

# Show progress
print("Starting transcription...")
transcript = transcriber.transcribe(audio_url)
print(f"Transcription complete. Duration: {transcript.audio_duration}s")
print("Analyzing with LeMUR...")

2. Poor Error Recovery

Add troubleshooting section:

## Common Issues

### Transcription Fails
- Check audio file format (supported: mp3, wav, m4a, etc.)
- Verify file accessibility (URL or local path)
- Ensure file size under 5GB

### LeMUR Analysis Incomplete
- Try shorter audio files (under 30 minutes recommended)
- Simplify phase definitions
- Check account credits

3. No Performance Guidance

Add:

## Best Practices

### Audio Optimization
- Use clear, high-quality recordings
- Minimize background noise
- Ensure speakers are distinguishable

### Phase Definition Tips
- Keep phase names simple and distinct
- Use 3-6 phases for optimal results
- Define phases that naturally occur in sequence

Additional Recommendations

1. Add Interactive Elements

## Try It Now
Use our sample audio file to test the segmentation:
```python
# Use AssemblyAI's sample customer service call
sample_url = "https://storage.googleapis.com/aai-web-samples/customer-service-call.wav"

2. Add Advanced Configuration

# Advanced LeMUR options
result = aai.Lemur().task(
    input_text=transcript.export_subtitles_vtt(),
    prompt=prompt,
    final_model=aai.LemurModel.claude3_5_sonnet,
    max_tokens=4000,  # Control response length
    temperature=0.1   # Lower temperature for more consistent results
)

3. Add Integration Examples

Show how to integrate with common workflows (CSV export, database storage, visualization tools).

This documentation needs substantial expansion to become truly user-friendly and production-ready. Focus on the missing error handling, better examples, and clearer explanations as top priorities.

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