Feedback: guides-lemur-pii-redaction

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/lemur-pii-redaction
Category: guides
Generated: 05/08/2025, 4:39:31 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:39:30 pm

Technical Documentation Analysis: AssemblyAI LeMUR PII Redaction Guide

Overall Assessment

This documentation provides a functional code example but lacks depth, context, and essential information that users need for successful implementation. The structure is basic and misses several critical elements for effective technical documentation.

Specific Issues and Recommendations

1. Missing Critical Information

Issues:

• No explanation of what LeMUR is or how it works
• Missing prerequisites beyond API key (Python version, dependencies)
• No information about rate limits, quotas, or usage constraints
• Missing error handling explanations
• No discussion of accuracy limitations or edge cases

Recommendations:

## What is LeMUR?
LeMUR (Leveraging Existing Models Using Retrieval) is AssemblyAI's framework that combines large language models with your transcribed audio data to perform advanced text processing tasks like PII redaction, summarization, and Q&A.

## Prerequisites
- Python 3.7 or higher
- AssemblyAI API key with LeMUR access
- Audio file accessible via URL or local file path
- Basic understanding of PII compliance requirements

## Limitations and Considerations
- LeMUR processes text in chunks; very long transcripts may require batching
- AI-based redaction may miss context-dependent PII or have false positives
- Always review redacted content for compliance requirements
- Rate limits: [specific limits] requests per minute

2. Unclear Explanations

Issues:

• The generate_ner function name is misleading (it’s doing PII detection, not NER specifically)
• No explanation of why sentence-by-sentence processing is used
• Missing explanation of LeMUR parameters (max_output_size, temperature, final_model)
• Unclear what happens if transcription fails

Recommendations:

def detect_pii_entities(text_segment):
    """
    Detects PII entities in a text segment using LeMUR.

    Args:
        text_segment (str): Text to analyze for PII

    Returns:
        list: List of detected PII entities

    Note: Processing sentence-by-sentence improves accuracy and
    reduces API payload size for long transcripts.
    """

Add parameter explanations:

### LeMUR Parameters Explained
- `max_output_size`: Maximum tokens in response (4000 = ~3000 words)
- `temperature`: Controls randomness (0.0 = deterministic, 1.0 = creative)
- `final_model`: AI model to use (claude3_5_sonnet recommended for accuracy)

3. Inadequate Examples

Issues:

• Only one basic example provided
• No example with actual input/output
• Missing examples for different types of audio content
• No example of handling edge cases

Recommendations: Add comprehensive examples:

## Example: Complete Workflow

### Input Audio Content
"Hi, my name is Sarah Johnson from Acme Corporation. You can reach me at sarah.johnson@acme.com or call me at 555-123-4567. I'm located at 123 Business Ave, San Francisco, CA 94105."

### Expected Output
"Hi, my name is #### ####### from #### ###########. You can reach me at ######################## or call me at ############. I'm located at ######################################."

### Example: Handling Different Content Types

```python
# For phone conversations
config = aai.TranscriptionConfig(
    language_code='en',
    speaker_labels=True,  # Useful for multi-speaker PII tracking
    punctuate=True,
    format_text=True
)

# For medical transcripts (additional PII types)
def detect_medical_pii(text):
    # Enhanced prompt for medical data
    prompt = '''
    Additional medical PII to detect:
    - Medical record numbers
    - Social security numbers
    - Date of birth
    - Insurance policy numbers
    '''

4. Poor Structure and Navigation

Issues:

• No table of contents
• Missing clear section hierarchy
• No “Next Steps” or related documentation links
• Quickstart and step-by-step are largely redundant

Recommendations: Restructure as:

# Redact PII from Audio Transcripts Using LeMUR

## Table of Contents
1. [Overview](#overview)
2. [Prerequisites](#prerequisites)
3. [Quick Start](#quick-start)
4. [Detailed Implementation](#detailed-implementation)
5. [Advanced Usage](#advanced-usage)
6. [Troubleshooting](#troubleshooting)
7. [Security Considerations](#security-considerations)
8. [Related Guides](#related-guides)

## Overview
Learn how to automatically detect and redact PII from audio transcripts using AssemblyAI's LeMUR framework...

## Quick Start
[Minimal working example with explanation]

## Detailed Implementation
[Step-by-step breakdown with explanations]

5. User Pain Points

Critical Issues:

1. No Error Handling Guidance

# Add comprehensive error handling
try:
    transcript = transcriber.transcribe(audio_url)
    if transcript.status == aai.TranscriptStatus.error:
        raise Exception(f"Transcription failed: {transcript.error}")
except Exception as e:
    print(f"Error during transcription: {e}")
    # Provide recovery steps

2. Security Concerns Not Addressed

## Security Best Practices
- Never log or store unredacted PII
- Use environment variables for API keys: `os.getenv('ASSEMBLYAI_API_KEY')`
- Consider data residency requirements for your use case
- Implement audit trails for PII processing

3. No Performance Guidance

## Performance Optimization
- For large files: Process in batches to avoid timeouts
- Use asynchronous processing for multiple files
- Consider caching results for repeated processing
- Monitor API usage to manage costs

4. Missing Validation

def validate_redaction_quality(original, redacted, entities):
    """
    Validates that redaction was successful and complete.

    Returns warnings for potential issues:
    - Entities that weren't redacted
    - Potential false positives
    - Formatting issues
    """

6. Additional Recommendations

Add Essential Sections:

• Troubleshooting: Common errors and solutions
• Testing: How to validate PII redaction effectiveness
• Compliance: GDPR, HIPAA, SOC 2 considerations
• Cost Management: Usage estimation and optimization
• Integration Examples: REST API, webhook implementations

Improve Code Quality:

• Add type hints
• Include docstrings
• Provide configuration options
• Add logging for debugging

User Experience:

• Add interactive code examples
• Provide downloadable sample files
• Include video walkthrough links
• Add FAQ section

This documentation needs significant enhancement to serve as effective technical guidance for production implementations.

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