Feedback: guides-custom-topic-tags

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/custom-topic-tags
Category: guides
Generated: 05/08/2025, 4:42:32 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:42:31 pm

Technical Documentation Analysis: Custom Topic Tags

Overall Assessment

This documentation provides a functional walkthrough but has several areas for improvement in clarity, completeness, and user experience. The content jumps into implementation without sufficient context and lacks important details that users will need.

Specific Issues and Recommendations

1. Missing Critical Information

Issues:

• No explanation of what LeMUR is or how it relates to topic tagging
• Missing prerequisites beyond API key (Python version, dependencies)
• No mention of supported audio formats or file size limits
• Pricing information is vague (“upgrade your account by adding a credit card”)
• No error handling examples

Recommendations:

## Prerequisites
- Python 3.7 or higher
- AssemblyAI account with LeMUR access (requires paid plan)
- Audio file in supported format (MP3, WAV, M4A, etc.)
- File size limit: 5GB maximum

## What is LeMUR?
LeMUR is AssemblyAI's Large Language Model that can analyze transcribed content to extract insights, answer questions, and perform tasks like topic classification.

2. Unclear Structure and Flow

Issues:

• “Get Started” section appears after “Quickstart” but contains prerequisite information
• Step-by-step instructions repeat information from quickstart without adding value
• No clear separation between setup and usage

Recommended Structure:

# Custom Topic Tags

## Overview
Brief explanation of what custom topic tags are and common use cases

## Prerequisites
Account setup, API key, and requirements

## Quick Start
Complete working example

## Detailed Guide
Step-by-step breakdown with explanations

## Advanced Usage
Multiple tags, confidence scores, custom prompts

## Troubleshooting
Common issues and solutions

3. Poor Code Examples

Issues:

• Hardcoded placeholder values without clear instructions
• No error handling
• No explanation of what each code block does
• Missing imports in step-by-step section

Improved Example:

import assemblyai as aai

# Set your API key (get from https://www.assemblyai.com/app)
aai.settings.api_key = "your-api-key-here"

# Transcribe audio file
audio_url = "https://example.com/audio.mp3"  # Replace with your audio URL

try:
    transcriber = aai.Transcriber()
    transcript = transcriber.transcribe(audio_url)

    # Wait for transcription to complete
    if transcript.status == aai.TranscriptStatus.error:
        print(f"Transcription failed: {transcript.error}")
        exit(1)

except Exception as e:
    print(f"Error during transcription: {e}")
    exit(1)

4. Missing User Experience Considerations

Issues:

• No explanation of processing time expectations
• No guidance on tag list optimization
• Missing information about result formats
• No validation examples

Recommendations: Add sections for:

## Best Practices
- Optimal number of tags (5-20 recommended)
- Clear, distinct topic descriptions
- How to handle edge cases

## Expected Results
- Processing time: 10-30 seconds for typical audio files
- Response format examples
- Confidence interpretation

5. Inadequate Examples and Use Cases

Issues:

• Only two tag list examples
• No real-world output examples
• Missing edge case handling

Enhanced Examples:

# Example with multiple potential tags
predicted_tags = transcript.lemur.task(
    prompt=f"""
    You are a content categorization assistant. Analyze the transcript and:
    1. Select the PRIMARY topic that best matches the content
    2. If the content clearly covers multiple topics, list up to 3 tags
    3. Include a confidence level (High/Medium/Low)

    Format your response as: "Primary: [tag], Secondary: [tag], Confidence: [level]"

    <topics_list>
    {tag_list}
    </topics_list>
    """
).response

6. Missing Troubleshooting Section

Add:

## Troubleshooting

### Common Issues
- **"Insufficient credits"**: Check your account balance and billing settings
- **"Transcription failed"**: Verify audio URL is publicly accessible
- **Inconsistent tagging**: Refine topic descriptions for better distinction

### API Limits
- Rate limits: X requests per minute
- Token limits: Y tokens per request
- File size limits: 5GB maximum

7. Improved Introduction

Current: Jumps directly into implementation Recommended:

# Custom Topic Tags

Automatically categorize your audio content using AssemblyAI's LeMUR AI model. This guide shows you how to:

- Set up custom topic categories for your content
- Process audio files and extract topic tags
- Handle different types of content (podcasts, support calls, meetings)

**Use Cases:**
- Content management systems
- Podcast categorization
- Support ticket routing
- Media library organization

Priority Improvements

1. Add prerequisites section with clear requirements
2. Include error handling in all code examples
3. Provide realistic examples with expected outputs
4. Add troubleshooting section for common issues
5. Restructure content flow for better learning progression
6. Add best practices for tag list creation
7. Include pricing details and usage limits

These improvements would significantly enhance user success rates and reduce support requests.

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