Feedback: lemur-customize-parameters

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/lemur/customize-parameters
Category: lemur
Generated: 05/08/2025, 4:29:55 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:29:54 pm

Technical Documentation Analysis: LeMUR Parameters Customization

Executive Summary

This documentation covers model customization for AssemblyAI’s LeMUR feature but has several critical issues that impact user experience, including missing context, unclear structure, incomplete explanations, and potential user confusion points.

Critical Issues

1. Missing Context & Prerequisites

Problem: The documentation jumps directly into parameter customization without establishing foundational context.

Impact: New users will be confused about what LeMUR is, how to get started, and what prerequisites are needed.

Recommendations:

• Add a brief introduction explaining what LeMUR is and its purpose
• Include prerequisites section: “Before customizing parameters, ensure you have…”
• Add links to getting started guides and basic LeMUR usage
• Explain the relationship between transcripts and LeMUR analysis

2. Unclear Document Structure & Navigation

Problem: The title suggests this covers “parameters” broadly, but content focuses heavily on model selection with other parameters feeling secondary.

Recommendations:

• Restructure with clearer hierarchy:

  # Customizing LeMUR Parameters
  
  ## Overview
  ## Model Selection
  ## Output Configuration
    - Maximum Output Size
    - Temperature Settings
  ## Input Customization
    - Custom Text Input
    - Multiple Transcripts
  ## Data Management
  ## Complete Examples

3. Inadequate Examples & Use Cases

Problem: Code examples are generic and don’t demonstrate real-world scenarios or explain when to use different parameters.

Recommendations:

• Add practical examples with complete context:

  # Example: Analyzing a customer support call with detailed output
  transcript = transcriber.transcribe("customer_call.mp3")
  
  result = transcript.lemur.task(
      prompt="Analyze this customer support call and identify: 1) Main issues raised, 2) Resolution provided, 3) Customer satisfaction level",
      final_model=aai.LemurModel.claude_sonnet_4_20250514,  # Use latest model for best reasoning
      max_output_size=2000,  # Allow detailed analysis
      temperature=0.3  # Lower temperature for consistent, factual analysis
  )

• Include use case guidance:

  • Creative tasks: Higher temperature (0.7-0.9), Claude Opus for complex reasoning
  • Factual analysis: Lower temperature (0.1-0.3), Claude Sonnet for efficiency
  • Quick summaries: Claude Haiku with moderate output size

4. Missing Critical Information

Key gaps:

• Default values: What happens if parameters aren’t specified?
• Error handling: What errors can occur and how to handle them?
• Rate limits: Are there API limits users should know about?
• Cost implications: How do different models and parameters affect pricing?
• Response structure: What does the API response look like?

Add sections:

## Default Behavior
If no `final_model` is specified, LeMUR uses Claude 3.5 Sonnet by default.
Default `max_output_size`: 1000 tokens
Default `temperature`: 0.5

## Error Handling
Common errors and solutions:
- `400 Bad Request`: Invalid model parameter
- `413 Payload Too Large`: Input exceeds 200,000 token limit
- `429 Too Many Requests`: Rate limit exceeded

## Cost Optimization
- Use Claude Haiku for simple tasks to reduce costs
- Set appropriate `max_output_size` to avoid unnecessary token usage
- Consider Claude 3.5 models for balanced cost/performance

5. Parameter Guidance Issues

Temperature Section:

• Current explanation is too brief
• Missing examples of temperature effects
• No guidance on optimal ranges for different use cases

Improved version:

## Temperature Control

Temperature controls response creativity and randomness (0.0-1.0):

- **0.0-0.3**: Highly focused, deterministic responses
  - Best for: Factual analysis, data extraction, classification
  - Example: "Extract key dates and numbers from this meeting transcript"

- **0.4-0.6**: Balanced creativity and consistency
  - Best for: Summaries, Q&A, general analysis
  - Example: "Summarize the main discussion points and decisions made"

- **0.7-1.0**: Creative, varied responses
  - Best for: Creative writing, brainstorming, open-ended analysis
  - Example: "Generate creative marketing ideas based on this customer feedback"

6. Code Quality & Completeness

Current issues:

• Variables like prompt, transcript_id appear without definition
• Missing imports and setup code
• Inconsistent error handling across languages
• No response processing examples

Improvements needed:

# Complete working example
import assemblyai as aai

# Setup (show this once, reference later)
aai.settings.api_key = "your-api-key"
transcriber = aai.Transcriber()

# Get transcript
transcript = transcriber.transcribe("path/to/audio.mp3")
# Wait for completion
transcript = transcriber.wait_for_completion(transcript)

# Use LeMUR with custom parameters
result = transcript.lemur.task(
    prompt="Provide a comprehensive summary of this audio, including key themes and important details.",
    final_model=aai.LemurModel.claude_sonnet_4_20250514,
    max_output_size=1500,
    temperature=0.4
)

# Handle the response
print(f"Analysis: {result.response}")
print(f"Request ID: {result.request_id}")  # For later data deletion

7. Model Selection Guidance

Problem: The model table lacks sufficient guidance on when to choose each model.

Add decision matrix:

## Choosing the Right Model

| Use Case | Recommended Model | Reason |
|----------|------------------|---------|
| Quick summaries | Claude 3.5 Haiku | Fastest, cost-effective |
| Detailed analysis | Claude 4 Sonnet | Best balance of speed/capability |
| Complex reasoning | Claude 4 Opus | Highest capability for difficult tasks |
| Multiple languages | Claude 4 Sonnet | Enhanced multilingual support |
| Legacy compatibility | Claude 3.5 Sonnet | Stable, widely tested |

8. User Experience Pain Points

Missing workflow guidance:

• No explanation of the typical LeMUR workflow
• Unclear how parameters interact with each other
• Missing troubleshooting section

Add workflow section:

## Typical Workflow

1. **Transcribe Audio**: Create transcript with appropriate features enabled
2. **Choose Model**: Select based on your use case complexity and budget
3. **Set Parameters**: Configure output size, temperature based on needs
4. **Submit Request**: Send to LeMUR API
5. **Process Results**: Handle response and optionally delete data

Additional Recommendations

9. Add Interactive Elements

• Parameter calculator to estimate costs
• Model comparison tool
• Template prompts for common use cases

10. Improve Cross-References

• Link to related documentation (pricing, API limits, other LeMUR features)
• Add “Next Steps” sections
• Include links to tutorials and advanced guides

11. Add Troubleshooting Section

## Troubleshooting

**Issue**: Response is too creative/inconsistent
**Solution**: Lower temperature to 0.1-0.3

**Issue**: Response is too brief
**Solution**: Increase max_output_size and be more specific in prompt

**Issue**: High API costs
**Solution**: Use Claude Haiku for simple tasks, set appropriate output limits

This documentation needs significant restructuring and additional content to provide a complete, user-friendly experience for developers working with LeMUR parameters.

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