Feedback: lemur-summarize-audio

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/lemur/summarize-audio
Category: lemur
Generated: 05/08/2025, 4:29:11 pm

Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:29:10 pm

Technical Documentation Analysis: Summarize Audio Data

Overall Assessment

This documentation provides comprehensive code examples across multiple programming languages, but suffers from structural issues, missing context, and unclear explanations that could frustrate users. Here’s my detailed analysis:

Critical Issues

1. Missing Prerequisites & Context

No explanation of what LeMUR is - Users are thrown into examples without understanding the technology
Missing cost information - Only mentions “credit card required” but no pricing details
No audio file requirements - What formats are supported? File size limits? Duration limits?
Unclear relationship between transcription and summarization steps

Inconsistent section progression - Jumps from “basic” to “specialized” without clear differentiation
Redundant code examples - The transcription step is repeated identically across sections
Poor section hierarchy - “Custom summary example (Advanced)” section has no actual content, just a link

Specific Improvement Recommendations

Add Missing Introduction Section

## What is LeMUR?
LeMUR (Leverage Large Language Models to Understand Recordings) applies AI models to your transcribed audio data. It can summarize meetings, extract insights, answer questions, and perform custom analysis tasks.

### Prerequisites
- AssemblyAI account with billing enabled
- Audio files in supported formats (MP3, WAV, MP4, etc.)
- Maximum file size: [specify limit]
- API key from your dashboard

Clarify the Two Approaches Early

## Choose Your Approach

**Option 1: Custom Prompts (Task Endpoint)**
- Full control over the summarization prompt
- Best for specific use cases or custom formatting
- Requires prompt engineering skills

**Option 2: Built-in Summary (Summary Endpoint)**
- Pre-optimized for general summarization
- More consistent, structured output
- Minimal configuration required

Fix Code Example Issues

Problem: Inconsistent variable names and missing error handling

Current:

audio_file = "https://assembly.ai/sports_injuries.mp3"
transcriber = aai.Transcriber()
transcript = transcriber.transcribe(audio_file)

Improved:

# Step 1: Transcribe audio (required before summarization)
audio_url = "https://assembly.ai/sports_injuries.mp3"  # Replace with your audio file
transcriber = aai.Transcriber()

try:
    transcript = transcriber.transcribe(audio_url)
    print(f"Transcription completed. ID: {transcript.id}")
except Exception as e:
    print(f"Transcription failed: {e}")
    exit(1)

Add Response Handling Examples

# Step 3: Handle the response
try:
    result = transcript.lemur.task(prompt, final_model=aai.LemurModel.claude_sonnet_4_20250514)

    print("Summary:")
    print(result.response)
    print(f"\nTokens used: {result.usage.input_tokens} input, {result.usage.output_tokens} output")

except Exception as e:
    print(f"Summarization failed: {e}")

Improve Parameter Documentation

Current: Vague mention of optional parameters Add:

### Key Parameters

| Parameter | Description | Example |
|-----------|-------------|---------|
| `final_model` | AI model to use | `anthropic/claude-sonnet-4-20250514` (recommended) |
| `context` | Additional background info | `"This is a sales call with a potential enterprise client"` |
| `answer_format` | Output structure | `"bullet points"`, `"TLDR"`, `"executive summary"` |
| `transcript_ids` | List of transcript IDs to analyze | `["transcript_123", "transcript_456"]` |

Add Practical Examples Section

## Common Use Cases

### Meeting Summaries
```python
prompt = """
Create an executive summary including:
1. Key decisions made
2. Action items with owners
3. Next meeting date
4. Unresolved issues
"""

Interview Analysis

prompt = """
Analyze this interview and provide:
- Candidate's key qualifications
- Strengths and concerns
- Recommendation (hire/no hire)
- Salary expectations mentioned
"""

Fix Missing Error Scenarios

Add section:

## Troubleshooting

### Common Errors
- **"Transcript not found"**: Ensure transcription completed before calling LeMUR
- **"Insufficient credits"**: Check your account balance in the dashboard
- **"Audio too long"**: LeMUR has a maximum token limit of [specify]
- **"Unsupported language"**: LeMUR works best with English transcripts

Add Performance Expectations

## What to Expect

- **Processing time**: 10-30 seconds for typical summaries
- **Audio length**: Works best with 5 minutes to 2 hours of audio
- **Cost**: Approximately $X per hour of audio (varies by model)
- **Languages**: Optimized for English, limited support for others

Structural Improvements

Reorganize Content Flow:

Introduction (What is LeMUR?)
Quick Start (Simplest working example)
Choose Your Method (Task vs Summary endpoints)
Detailed Examples (Current code samples, improved)
Customization (Parameters, prompts)
Use Cases (Practical examples)
Troubleshooting
API Reference (Links)

## In This Guide
- [Quick Start](#quick-start) - Get your first summary in 5 minutes
- [Task Endpoint](#task-endpoint) - Custom prompts and formatting
- [Summary Endpoint](#summary-endpoint) - Pre-built summarization
- [Advanced Examples](#advanced-examples) - Complex use cases
- [Troubleshooting](#troubleshooting) - Common issues and solutions

Minor but Important Fixes

Consistent code highlighting - Some examples lack proper syntax highlighting
Add output examples for each code sample, not just one
Fix broken internal links - Several cookbook references need verification
Add rate limiting information - How many requests per minute?
Specify model differences - Why choose Claude over other models?

These improvements would transform this from a code-heavy reference into a user-friendly guide that helps developers succeed quickly while providing the depth needed for advanced use cases.