Feedback: lemur-ask-questions

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/lemur/ask-questions
Category: lemur
Generated: 05/08/2025, 4:29:46 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:29:45 pm

Technical Documentation Analysis: LeMUR Q&A Guide

Overall Assessment

This documentation provides comprehensive code examples but has several areas for improvement in clarity, structure, and user experience. Here’s my detailed feedback:

🔍 Missing Information

Critical Gaps

1. Cost information: No mention of pricing, token usage, or billing implications
2. Rate limits: Missing API rate limiting details and best practices
3. Error handling: Limited error scenarios and troubleshooting guidance
4. Response time expectations: No SLA or typical processing times mentioned
5. Audio file limitations: Missing file size, format, and duration constraints
6. Model comparison: No guidance on when to use different models

Technical Details

• Authentication troubleshooting: No guidance for API key issues
• Webhook support: No mention of async processing options
• Batch processing: Missing information about handling multiple files
• Regional availability: No mention of geographic restrictions

📚 Unclear Explanations

Terminology Issues

1. “LeMUR” - Never explained what the acronym stands for or its purpose
2. “final_model” parameter - Unclear why it’s called “final” and what alternatives exist
3. Context vs. Prompt distinction - The difference isn’t clearly explained

Process Flow

• The relationship between transcription and Q&A steps could be clearer
• Missing explanation of how the underlying transcript provides context

💡 Better Examples Needed

Current Example Limitations

1. Single question focus: Most examples use only one simple question
2. Generic scenarios: Examples don’t show real-world business use cases
3. Limited output variety: Missing examples of different response formats

Suggested Improvements

## Real-World Examples

### Customer Service Analysis
```python
prompt = """
Analyze this customer service call and answer:
1. What was the main issue reported?
2. Was the issue resolved? (Yes/No)
3. What was the customer's satisfaction level? (Scale 1-5)
4. List any follow-up actions mentioned
"""

Meeting Minutes Extraction

questions = [
    {
        "question": "What decisions were made in this meeting?",
        "answer_format": "bulleted list"
    },
    {
        "question": "Who are the action item owners?",
        "answer_format": "name: action item pairs"
    }
]

## 🏗️ **Improved Structure**

### Recommended Organization
```markdown
# Ask Questions About Your Audio Data

## Overview
- What is LeMUR Q&A
- Use cases and benefits
- Prerequisites and costs

## Quick Start (5-minute guide)
- Simple example with explanation
- Expected output

## Methods Comparison
| Method | Best For | Pros | Cons |
|--------|----------|------|------|
| Basic Task | Custom prompts | Flexible | Requires prompt engineering |
| Q&A Endpoint | Structured questions | No prompt engineering | Less flexible |

## Detailed Implementation
- Basic Q&A
- Specialized endpoint
- Advanced examples

## Best Practices
- Prompt optimization
- Error handling
- Performance tips

## Troubleshooting
- Common errors
- Debug strategies

⚠️ User Pain Points

Major Issues Identified

1. Overwhelming code examples: 6+ language tabs make the page hard to scan

   • Solution: Default to Python SDK, collapse others

2. Missing prerequisites checklist:

   ## Before You Begin
   - [ ] AssemblyAI account with credit card
   - [ ] API key configured
   - [ ] Audio file under 5GB
   - [ ] Supported audio format (MP3, WAV, etc.)

3. No success validation: Users don’t know if their implementation worked correctly

   • Solution: Add “Verify Your Setup” section

4. Error handling gaps: Code examples don’t show how to handle failures

   # Add this to examples
   try:
       result = transcript.lemur.task(prompt)
       print(result.response)
   except Exception as e:
       print(f"LeMUR processing failed: {e}")
       # Check transcript quality, try simpler prompt, etc.

🚀 Specific Actionable Improvements

Immediate Fixes

1. Add cost warning box:

   <Warning>
   LeMUR operations consume credits based on audio length and model choice.
   See [pricing page] for current rates.
   </Warning>

2. Create a decision tree:

   ## Which Method Should I Use?
   - **Simple questions about content** → Q&A Endpoint
   - **Custom analysis or formatting** → Task Endpoint
   - **Multiple structured questions** → Q&A Endpoint with question arrays

3. Add troubleshooting section:

   ## Common Issues
   | Error | Cause | Solution |
   |-------|-------|----------|
   | "Insufficient credits" | Account billing | Add payment method |
   | "Transcript not found" | Invalid transcript_id | Check transcription status |
   | "Poor quality response" | Unclear prompt | See prompt engineering guide |

Enhanced User Experience

4. Interactive elements:

   • Add “Try it” buttons linking to a playground
   • Include expected processing times
   • Show token usage estimates

5. Progressive disclosure:

   • Start with SDK examples (simpler)
   • Move raw API calls to expandable sections
   • Group advanced options separately

6. Better cross-references:

   • Link to specific error codes documentation
   • Reference related features (summarization, etc.)
   • Connect to prompt engineering best practices

This documentation would benefit significantly from user testing to identify real-world pain points and usage patterns.

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