Feedback: guides-timestamped-transcripts

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/timestamped-transcripts
Category: guides
Generated: 05/08/2025, 4:35:34 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:35:33 pm

Technical Documentation Analysis: Extract Transcript Quotes with LeMUR

Overall Assessment

This documentation covers a useful feature but has several structural and clarity issues that could create friction for users. Here’s my detailed feedback:

🔴 Critical Issues

1. Misleading Title and URL Mismatch

• Problem: The URL suggests “timestamped-transcripts” but the content is about “Extract Transcript Quotes with LeMUR”
• Impact: Users searching for timestamp functionality may not find this, and those who do may be confused
• Fix: Either update the URL or clarify the title to better match the content focus

2. Missing Prerequisites and Setup

• Problem: No clear system requirements, Python version requirements, or environment setup
• Fix: Add a prerequisites section:

## Prerequisites
- Python 3.7 or higher
- An AssemblyAI account with API credits
- Basic familiarity with Python and API concepts

🟡 Structure and Organization Issues

3. Redundant Code Sections

• Problem: The Quickstart and Step-by-Step sections contain nearly identical code
• Fix: Restructure as:
  • Quick Example (minimal code with explanation)
  • Detailed Walkthrough (with explanations for each step)
  • Complete Example (full working code)

4. Poor Information Hierarchy

• Problem: Key concepts (LeMUR, input_text parameter) are mentioned but not explained
• Fix: Add an “Overview” section explaining:
  • What LeMUR is and when to use it
  • How the input_text parameter works
  • Use cases for this approach vs. standard transcription

🟡 Missing Information

5. No Error Handling or Troubleshooting

• Add: Common error scenarios and solutions:

try:
    transcript = transcriber.transcribe(audio_url)
    if transcript.status == assemblyai.TranscriptStatus.error:
        print(f"Transcription failed: {transcript.error}")
        return
except Exception as e:
    print(f"Error during transcription: {e}")

6. Missing Input Format Details

• Problem: Users don’t understand what formats are accepted for input_text
• Fix: Add section explaining:
  • Supported input formats
  • Character limits
  • How the tuple format works: (text, start_time, end_time)

7. No Cost or Performance Information

• Add:
  • Typical processing times
  • Cost implications of using LeMUR
  • Rate limits and best practices

🟡 Examples and Use Cases

8. Limited Example Scenarios

• Current: Only one use case (finding engaging quotes)
• Add: Multiple examples:

# Example 1: Extract key topics with timestamps
questions=[{
    "question": "What are the main topics discussed?",
    "answer_format": "Topic name with relevant quote and timestamp"
}]

# Example 2: Find specific information
questions=[{
    "question": "What statistics or numbers are mentioned?",
    "answer_format": "Statistic with exact quote and timestamp"
}]

9. No Alternative Audio Sources

• Problem: Only uses one GitHub-hosted audio file
• Fix: Show examples with:
  • Local file paths
  • Different audio formats
  • User-uploaded content

🟡 User Experience Issues

10. No Output Format Explanation

• Problem: Users don’t understand the response structure
• Fix: Add section explaining:

# Response structure explanation
result.response[0].answer  # First question's answer
len(result.response)       # Number of questions answered

# How to parse multiple quotes from the response
quotes = result.response[0].answer.split('\n')
for quote in quotes:
    # Parse quote format: ("text", start_ms, end_ms)

11. Missing Customization Options

• Add: Examples of:
  • Different LeMUR models and when to use them
  • Custom prompting techniques
  • Filtering options (e.g., minimum quote length)

12. No Integration Guidance

• Add: How to integrate this into common workflows:
  • Saving results to files
  • Converting timestamps to different formats
  • Combining with other AssemblyAI features

🟢 Recommended Structure

# Extract Timestamped Quotes from Audio using LeMUR

## Overview
Brief explanation of what this guide covers and when to use this approach

## Prerequisites
System requirements and account setup

## Quick Example
Minimal working code with brief explanation

## How It Works
Explanation of LeMUR, input_text parameter, and the process flow

## Step-by-Step Tutorial
Detailed walkthrough with explanations

## Customization Options
Different models, prompt strategies, output formats

## Common Use Cases
Multiple examples for different scenarios

## Error Handling
Common issues and solutions

## Best Practices
Performance tips, cost optimization, rate limits

## Next Steps
Related features and advanced techniques

🟢 Additional Suggestions

13. Add Interactive Elements

• Link to a live Colab notebook
• “Try it yourself” section with sample audio files
• Interactive parameter explorer

14. Cross-Reference Related Features

• Link to general LeMUR documentation
• Reference standard transcription guides
• Connect to timestamp-specific features

This documentation has good foundational content but needs significant restructuring and additional context to provide a smooth user experience.

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