Feedback: guides-task-endpoint-custom-summary

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/task-endpoint-custom-summary
Category: guides
Generated: 05/08/2025, 4:36:19 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:36:18 pm

Technical Documentation Analysis: Custom Summary Using LeMUR

Overall Assessment

This documentation provides a functional walkthrough but lacks depth in several critical areas. While the basic implementation is clear, users may struggle with customization, error handling, and understanding the broader context of the feature.

Critical Missing Information

1. Prerequisites and Requirements

• Missing: Minimum Python version requirements
• Missing: Complete list of dependencies
• Missing: Account tier requirements beyond “upgrade with credit card”
• Add: Clear system requirements section

2. Error Handling and Troubleshooting

# Add this comprehensive error handling example
try:
    transcript = aai.Transcriber().transcribe(audio_url)
    if transcript.status == aai.TranscriptStatus.error:
        print(f"Transcription failed: {transcript.error}")
        return

    result = transcript.lemur.task(prompt, final_model=aai.LemurModel.claude3_5_sonnet)

except aai.AuthenticationError:
    print("Invalid API key or insufficient permissions")
except aai.RateLimitError:
    print("Rate limit exceeded. Please wait before retrying.")
except Exception as e:
    print(f"Unexpected error: {e}")

3. API Response Structure

# Document the complete response object
print(f"Summary: {result.response}")
print(f"Request ID: {result.request_id}")
print(f"Usage: {result.usage}")  # Token usage information

Structural Improvements

1. Reorganize Content Flow

# Generate Custom Summaries Using LeMUR

## Overview
Brief explanation of what LeMUR does and use cases

## Prerequisites
- Python 3.7+
- AssemblyAI account with LeMUR access
- API key from dashboard

## Quick Start
[Current quickstart code with error handling]

## Detailed Guide
[Step-by-step breakdown]

## Customization Options
[Prompt engineering, models, formats]

## Advanced Examples
[Multiple examples for different use cases]

## Troubleshooting
[Common issues and solutions]

## API Reference
[Complete parameter documentation]

Unclear Explanations Requiring Clarification

1. LeMUR Model Selection

# Add explanation of available models
available_models = {
    aai.LemurModel.claude3_5_sonnet: "Best for complex analysis, higher cost",
    aai.LemurModel.claude3_haiku: "Faster, cost-effective for simple tasks",
    # Add other available models with use case guidance
}

2. Prompt Engineering Guidance

### Prompt Best Practices

**Effective Prompt Structure:**
- Clear role definition
- Specific output requirements
- Format specifications
- Constraints and limitations

**Example Variations:**
```python
# For meeting summaries
meeting_prompt = """You are a meeting secretary. Create an executive summary focusing on:
- Key decisions made
- Action items assigned
- Next steps identified
Format as numbered sections."""

# For interview summaries
interview_prompt = """Summarize this interview highlighting:
- Main topics discussed
- Key insights shared
- Notable quotes
Keep under 200 words."""

Better Examples Needed

1. Multiple Use Cases

# Add these diverse examples:

# 1. Customer service call summary
customer_service_prompt = """
Summarize this customer service interaction:
- Customer issue/concern
- Solution provided
- Customer satisfaction level
- Follow-up required (yes/no)
"""

# 2. Podcast episode summary
podcast_prompt = """
Create a podcast episode summary with:
- Main topic and guests
- Key discussion points (3-5 bullets)
- Notable quotes or insights
- Target audience takeaways
"""

# 3. Legal deposition summary
legal_prompt = """
Provide a legal deposition summary including:
- Parties involved
- Key testimony points
- Objections raised
- Critical admissions or denials
"""

2. Format Options Examples

format_examples = {
    "Executive Summary": "3-paragraph format with overview, details, conclusion",
    "Bullet Points": "Hierarchical bullet structure",
    "Timeline": "Chronological event sequence",
    "Q&A Format": "Question and answer pairs",
    "Table": "Structured data in tabular format"
}

User Pain Points to Address

1. File Size and Processing Limits

### Limitations and Constraints
- Maximum audio file size: [specify limit]
- Processing time estimates: [provide ranges]
- Transcript length limits for LeMUR: [specify]
- Rate limiting: [requests per minute/hour]

2. Cost Transparency

# Add cost estimation helper
def estimate_cost(audio_duration_minutes, transcript_length_words):
    """
    Estimate processing costs for transcription + LeMUR summary

    Args:
        audio_duration_minutes: Length of audio file
        transcript_length_words: Estimated word count

    Returns:
        dict: Cost breakdown
    """
    transcription_cost = audio_duration_minutes * 0.00065  # Current rate
    lemur_cost = (transcript_length_words / 1000) * 0.015  # Estimated

    return {
        "transcription": transcription_cost,
        "lemur": lemur_cost,
        "total": transcription_cost + lemur_cost
    }

3. Async Processing for Large Files

# Add async example for large files
import asyncio

async def process_large_file(audio_url):
    """Handle large files with async processing"""
    config = aai.TranscriptionConfig(
        speech_model=aai.SpeechModel.best  # For better accuracy
    )

    transcriber = aai.Transcriber(config=config)
    transcript = await transcriber.transcribe_async(audio_url)

    # Poll for completion
    while transcript.status == aai.TranscriptStatus.processing:
        await asyncio.sleep(5)
        transcript = await transcript.get_async()

    return transcript

Additional Recommendations

1. Add Interactive Elements

• Link to live demo or playground
• “Try it now” button with sample audio
• Interactive prompt builder

2. Cross-References

• Link to related LeMUR endpoints (Q&A, Topics)
• Reference prompt engineering best practices
• Connect to audio preprocessing guides

3. Complete Code Templates

Provide production-ready templates with:

• Environment variable management
• Logging configuration
• Configuration file usage
• Batch processing examples

This documentation would significantly benefit from these improvements to provide a more comprehensive, user-friendly experience that anticipates and addresses real-world implementation challenges.

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