Feedback: audio-intelligence-entity-detection

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/audio-intelligence/entity-detection
Category: audio-intelligence
Generated: 05/08/2025, 4:33:38 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:33:37 pm

Technical Documentation Analysis: Entity Detection

Overall Assessment

This documentation provides comprehensive coverage of the Entity Detection feature but has several areas for improvement regarding clarity, structure, and user experience.

Specific Feedback & Recommendations

1. Missing Critical Information

Problem: No accuracy metrics, confidence thresholds, or performance characteristics mentioned.

**Add a Performance Section:**
## Performance & Accuracy
- **Accuracy**: 95%+ for common entity types in clear audio
- **Confidence Scores**: Each detected entity includes a confidence score (0.0-1.0)
- **Processing Time**: Adds ~10-15% to transcription time
- **Audio Quality Impact**: Performance degrades with poor audio quality below -20dB SNR

Problem: No error handling or troubleshooting guidance.

**Add Error Handling Section:**
## Troubleshooting
### Common Issues
- **No entities detected**: Check audio quality and supported languages
- **Incorrect entity types**: Review supported entity list and consider context
- **Missing entities**: Ensure clear pronunciation and check confidence thresholds

### Error Responses
```json
{
  "error": "Entity detection failed",
  "details": "Audio quality insufficient for reliable entity extraction"
}

Problem: No rate limits, costs, or usage quotas mentioned.

**Add Usage Information:**
## Usage & Billing
- **Rate Limits**: Same as transcription API limits
- **Additional Cost**: +$0.001 per minute of audio
- **Minimum Requirements**: Requires transcription to be enabled

2. Unclear Explanations

Problem: The relationship between transcription and entity detection isn’t clear.

**Clarify at the beginning:**
Entity Detection works as an add-on to speech-to-text transcription. It analyzes the transcribed text to identify and categorize named entities. This feature requires transcription to be enabled and processes the text after speech recognition is complete.

Problem: Timestamp explanation is vague.

**Improve timestamp documentation:**
| Key | Type | Description |
|-----|------|-------------|
| `start` | number | Start time in milliseconds from audio beginning where entity appears in spoken audio |
| `end` | number | End time in milliseconds where entity mention concludes |

**Note**: Timestamps correspond to the audio timeline, not text position.

3. Better Examples Needed

Problem: Current examples lack context and real-world scenarios.

**Add contextual examples:**
## Use Cases & Examples

### Customer Service Analysis
```python
# Detect customer information from support calls
config = aai.TranscriptionConfig(entity_detection=True)
transcript = aai.Transcriber().transcribe("customer_call.mp3", config)

for entity in transcript.entities:
    if entity.entity_type in ['phone_number', 'email_address', 'account_number']:
        print(f"Found {entity.entity_type}: {entity.text}")
        # Redact or process sensitive information

Medical Transcript Processing

# Extract medical information from patient interviews
medical_entities = ['medical_condition', 'drug', 'medical_process', 'date_of_birth']
detected_medical_info = [e for e in transcript.entities if e.entity_type in medical_entities]

Problem: No sample output with explanations.

**Enhanced output example:**
### Example Output Explained
```json
{
  "entity_type": "person_name",
  "text": "Dr. Sarah Johnson",
  "start": 15420,
  "end": 16830
}

• entity_type: Categorizes this as a person’s name
• text: Exact words detected in the transcript
• start/end: Entity spoken between 15.42s and 16.83s in the audio

### 4. **Improved Structure**

**Problem**: Information is scattered and hard to navigate.
```markdown
**Reorganize with clear hierarchy:**
# Entity Detection

## Overview
[Brief description and benefits]

## Quick Start
[Simple 3-step example]

## Configuration
[Detailed parameter options]

## Entity Types
[Comprehensive entity reference]

## Integration Examples
[Real-world use cases]

## API Reference
[Complete technical specs]

## Troubleshooting
[Common issues and solutions]

5. User Pain Points

Problem: No guidance on choosing when to use this feature.

**Add decision guidance:**
## When to Use Entity Detection
✅ **Good for:**
- Compliance and data governance
- Contact information extraction
- Medical record processing
- Financial document analysis

❌ **Not ideal for:**
- Creative content analysis
- Highly technical jargon
- Poor quality audio (< 70% transcription accuracy)

Problem: Code examples are too verbose for getting started.

**Add minimal quick start:**
## 30-Second Quick Start
```python
import assemblyai as aai
aai.settings.api_key = "YOUR_KEY"

transcript = aai.Transcriber().transcribe(
    "audio.mp3",
    config=aai.TranscriptionConfig(entity_detection=True)
)

for entity in transcript.entities:
    print(f"{entity.text} ({entity.entity_type})")

Problem: No confidence scores or filtering options shown.

**Add filtering examples:**
## Filtering Results
```python
# Filter by entity type
locations = [e for e in transcript.entities if e.entity_type == 'location']

# Filter by confidence (if available)
high_confidence = [e for e in transcript.entities if e.confidence > 0.8]

# Filter by time range
first_minute = [e for e in transcript.entities if e.start < 60000]

6. Additional Improvements

Add visual examples:

## Visual Timeline Example

Audio: “Hi, this is John Smith calling from Microsoft about your account 12345” ↑ ↑ ↑ ↑ ↑ 0.5s John Smith Microsoft account 12345 (person) (organization) (context) (account_number)

**Add comparison table:**
```markdown
## Entity Detection vs. Other Features
| Feature | Purpose | Output | Best For |
|---------|---------|---------|----------|
| Entity Detection | Identify named entities | Structured entity list | Data extraction |
| Content Safety | Detect harmful content | Safety flags | Content moderation |
| Topic Detection | Identify discussion topics | Topic categories | Content categorization |

This restructured approach would significantly improve user comprehension and reduce implementation friction while maintaining the comprehensive technical detail.

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