Feedback: getting-started-slam-1

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/getting-started/slam-1
Category: getting-started
Generated: 05/08/2025, 4:30:35 pm

Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:30:34 pm

Technical Documentation Analysis: Slam-1 Getting Started Guide

Overall Assessment

This documentation provides a solid foundation but has several areas that need improvement for better user experience and completeness. Here’s my detailed analysis:

🚨 Critical Missing Information

1. Prerequisites Section

Issue: No prerequisites or setup requirements mentioned. Impact: Users may fail immediately without proper setup.

Recommendation: Add a prerequisites section before Quick Start:

## Prerequisites

Before using Slam-1, ensure you have:
- An AssemblyAI API key ([Get one here](link))
- Python 3.7+ or Node.js 14+ installed
- Basic familiarity with REST APIs

2. Authentication Details

Issue: API key format and acquisition process unclear. Impact: Users don’t know how to get or format their API key.

Recommendation:

Explain API key format (e.g., “YOUR_API_KEY should look like: abcd1234...”)
Link to account setup/API key generation
Show header format more clearly

3. Error Handling

Issue: Limited error scenarios covered. Impact: Users struggle when things go wrong.

Recommendation: Add comprehensive error handling section:

## Common Errors and Solutions

| Error Code | Cause | Solution |
|------------|-------|----------|
| 401 | Invalid API key | Check your API key format |
| 400 | Invalid audio URL | Ensure URL is publicly accessible |
| 429 | Rate limit exceeded | Implement exponential backoff |

📋 Structure and Organization Issues

1. Logical Flow Problems

Current flow: Overview → Quick Start → Fine-tuning → Feedback Issue: Fine-tuning feels like advanced usage but appears immediately after basic usage.

Recommendation: Restructure as:

1. Overview
2. Prerequisites
3. Quick Start
4. Understanding the Response
5. Working with Local Files
6. Advanced Features (Fine-tuning)
7. Troubleshooting
8. Next Steps
9. Feedback

2. Missing Response Documentation

Issue: No explanation of the API response structure. Impact: Users don’t understand what they’re getting back.

Recommendation: Add response documentation:

## Understanding the Response

The transcription response includes:
- `id`: Unique transcript identifier
- `status`: Current processing status (`queued`, `processing`, `completed`, `error`)
- `text`: Final transcription (when completed)
- `confidence`: Overall confidence score
- `audio_duration`: Length of audio in seconds

Example response:
```json
{
  "id": "abc123",
  "status": "completed",
  "text": "Your transcribed text here",
  "confidence": 0.95,
  "audio_duration": 120.5
}

🔧 Code Examples Issues

1. Incomplete Error Handling

Issue: Python example has basic error handling, JavaScript doesn’t. Impact: Inconsistent user experience across languages.

Recommendation: Standardize error handling across all examples:

// Add to JavaScript example
if (response.status !== 200) {
  console.error(`Error: ${response.status}`, response.data);
  throw new Error(`API request failed: ${response.status}`);
}

2. Missing Imports/Dependencies

Issue: JavaScript example uses axios without installation instructions. Impact: Code won’t run out of the box.

Recommendation: Add installation instructions:

<Note title="Dependencies">
For JavaScript: `npm install axios`
For Python: `pip install requests`
</Note>

3. Hardcoded Values

Issue: Using a specific audio URL without explaining why. Impact: Users may think they must use that exact file.

Recommendation:

Explain the sample file purpose
Show how to use different URLs
Provide sample files for testing

🎯 User Experience Pain Points

1. Polling Mechanism Unclear

Issue: No explanation of why polling is necessary or how long it takes. Impact: Users don’t understand the async nature.

Recommendation: Add explanation:

<Info title="Why Polling?">
Transcription is asynchronous. Processing time varies by audio length:
- Short files (< 1 minute): ~10-30 seconds
- Medium files (1-10 minutes): ~30-120 seconds
- Long files (> 10 minutes): ~2-5 minutes

The polling interval of 3 seconds balances responsiveness with API efficiency.
</Info>

2. Vague Feature Benefits

Issue: “unprecedented accuracy” and “superior transcription” are marketing terms without substance. Impact: Technical users want concrete information.

Recommendation: Provide specific benefits:

## Key Improvements in Slam-1

- **Contextual Understanding**: Better handling of homophones (e.g., "their" vs "there")
- **Domain Adaptation**: Improved accuracy for technical terminology
- **Semantic Awareness**: Understanding of phrase relationships
- **Reduced Hallucination**: Fewer incorrect word insertions

📝 Content Clarity Issues

1. Fine-tuning Section Confusion

Issue: The explanation of how keyterms_prompt works is verbose and unclear. Impact: Users don’t understand the practical application.

Recommendation: Simplify and restructure:

## Fine-tuning with Key Terms

Improve accuracy by providing domain-specific terms that may appear in your audio.

**How it works**: Slam-1 uses these terms to better understand context, improving transcription of:
- The exact terms you provide
- Related terminology
- Contextually similar phrases

**Usage**:
- Up to 1000 terms total
- Maximum 6 words per phrase
- Include proper nouns, technical terms, and domain-specific vocabulary

2. Token Limit Explanation

Issue: The tokenization explanation is confusing and technical. Impact: Users don’t know how to count tokens practically.

Recommendation: Provide practical guidance:

<Note title="Practical Token Guidelines">
**Rule of thumb**: Each word ≈ 1-2 tokens
- "therapy" = ~1 token
- "Wellbutrin XL 150mg" = ~4 tokens
- Proper nouns may use more tokens

**Stay well under 1000 terms** to avoid hitting limits. If approaching the limit, prioritize your most important terms.
</Note>

🚀 Recommended Additions

1. Quick Success Path

Add a minimal working example at the top:

## 30-Second Test

Try Slam-1 in under a minute:

```bash
curl -X POST https://api.assemblyai.com/v2/transcript \
  -H "Authorization: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"audio_url": "https://assembly.ai/sports_injuries.mp3", "speech_model": "slam-1"}'

2. Comparison Guide

## When to Use Slam-1

| Use Slam-1 when: | Use standard models when: |
|-------------------|---------------------------|
| High accuracy is critical | Speed is priority |
| Complex terminology present | Simple conversational audio |
| Context matters | Cost optimization needed |

3. Next Steps Section

## Next Steps

- [Real-time transcription with Slam-1](link)
- [Advanced configuration options](link)
- [Integrating with your application](link)
- [Rate limits and pricing](link)

📊 Priority Recommendations

High Priority: Add prerequisites, fix error handling, clarify polling
Medium Priority: Restructure content flow, add response documentation
Low Priority: Add comparison guides, enhance examples with more languages

This documentation has good bones but needs these improvements to provide a smooth user experience and reduce support burden.