Feedback: guides-counting-tokens

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/counting-tokens
Category: guides
Generated: 05/08/2025, 4:42:34 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:42:33 pm

Technical Documentation Feedback: Token Cost Estimation Guide

🚨 Critical Issues

1. Misleading Core Information

• Issue: The guide states “LeMUR counts tokens based solely on character count” but then uses character count as token count (1:1 ratio), which is incorrect for LLM tokenization
• Impact: Users will get inaccurate cost estimates
• Fix: Either clarify that AssemblyAI uses character-based pricing OR provide actual token counting methodology

2. Missing Essential Prerequisites

• No mention of required AssemblyAI account setup
• No explanation of where to get API keys
• No error handling for common authentication issues

📝 Content Completeness Issues

Missing Information:

1. Cost calculation context: Total cost breakdown (input + output + base fees)
2. Token vs. character explanation: Clear distinction and why AssemblyAI uses characters
3. Prompt token calculation: Promised but not demonstrated
4. Rate limits and quotas: Important for cost planning
5. Error scenarios: What happens if transcription fails?

Incomplete Examples:

# Current example uses hardcoded values - add dynamic pricing
# Better approach:
PRICING = {
    "claude_3_5_sonnet": 0.003,
    "claude_opus": 0.015,
    "claude_haiku": 0.00025
}

def calculate_costs(character_count, pricing_dict):
    """Calculate costs for different LeMUR models"""
    count_in_thousands = character_count / 1000
    return {model: price * count_in_thousands
            for model, price in pricing_dict.items()}

🏗️ Structure Improvements

Recommended Reorganization:

# Estimate LeMUR Token Costs

## Overview
- What is LeMUR?
- Why token counting matters
- Character-based vs token-based pricing explanation

## Prerequisites
- API key setup
- Account requirements
- Installation

## Quick Start
[Current quickstart with error handling]

## Detailed Guide
### 1. Basic Transcription and Counting
### 2. Adding Prompt Costs
### 3. Output Token Estimation
### 4. Total Cost Calculation

## Advanced Usage
- Batch processing
- Cost optimization tips
- Different audio formats

## Troubleshooting
## FAQ

🔧 Specific Code Improvements

1. Add Error Handling

import assemblyai as aai

# Validate API key
if not aai.settings.api_key or aai.settings.api_key == "YOUR_API_KEY":
    raise ValueError("Please set your AssemblyAI API key")

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

2. Create Reusable Functions

def estimate_lemur_costs(transcript_text, prompt_text="", max_output_tokens=0):
    """
    Estimate total LeMUR costs including input, prompt, and output tokens.

    Args:
        transcript_text (str): The transcribed text
        prompt_text (str): Your LeMUR prompt
        max_output_tokens (int): Expected output token count

    Returns:
        dict: Cost breakdown by model
    """
    # Implementation here

3. Add Input Validation

def validate_inputs(audio_source):
    """Validate audio source before processing"""
    if isinstance(audio_source, str):
        if audio_source.startswith(('http://', 'https://')):
            # Validate URL accessibility
            pass
        else:
            # Validate file path exists
            pass

💡 User Experience Improvements

1. Add Interactive Elements

# Cost calculator function
def interactive_cost_calculator():
    """Interactive cost estimation tool"""
    audio_url = input("Enter audio URL or file path: ")
    prompt = input("Enter your LeMUR prompt (optional): ")
    max_output = int(input("Expected output tokens (optional): ") or 0)

    # Calculate and display results

2. Provide Multiple Examples

• Short audio file (< 1 minute)
• Medium audio file (5-10 minutes)
• Long audio file (30+ minutes)
• Batch processing scenario

3. Add Cost Optimization Tips

## Cost Optimization Tips

1. **Choose the right model**: Haiku for simple tasks, Sonnet for complex analysis
2. **Optimize transcription**: Use appropriate speech model for your audio type
3. **Batch processing**: Process multiple files in one session
4. **Prompt engineering**: Write concise, effective prompts

🚀 Additional Recommendations

1. Add Troubleshooting Section

## Common Issues

**"Invalid API Key" Error**
- Verify key from dashboard
- Check environment variable setup

**High Unexpected Costs**
- Review max_output_size settings
- Check for repeated processing

**Transcription Failures**
- Verify audio format support
- Check file accessibility

2. Include Performance Considerations

• File size limits
• Processing time estimates
• Concurrent request limits

3. Add Related Resources

## Next Steps
- [LeMUR Advanced Configuration](link)
- [Optimizing Transcription Quality](link)
- [Batch Processing Guide](link)

4. Version and Date Information

Add pricing last updated date and SDK version compatibility.

Summary Priority Fixes:

1. Critical: Fix token counting explanation
2. High: Add error handling and validation
3. High: Complete the prompt cost calculation example
4. Medium: Restructure for better flow
5. Medium: Add troubleshooting section
6. Low: Add interactive elements and optimization tips

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