Feedback: guides-lemur

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/lemur
Category: guides
Generated: 05/08/2025, 4:39:25 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:39:24 pm

Technical Documentation Analysis: LeMUR Guide

Overall Assessment

This overview page serves as a directory but lacks critical foundational information that users need before diving into specific workflows. The structure is logical but incomplete for an effective starting point.

Critical Missing Information

1. Fundamental Concepts

## What is LeMUR?
LeMUR (Large Language Model Universal Response) is AssemblyAI's framework that applies Large Language Models to audio transcripts. It enables you to:
- Ask questions about transcribed audio content
- Generate summaries of meetings or calls
- Extract structured data from conversations
- Analyze speaker sentiment and behavior

### Key Components
- **Transcript Processing**: Converts audio to text first
- **LLM Integration**: Applies AI models to analyze the text
- **Structured Output**: Returns results in various formats (JSON, text, etc.)

2. Prerequisites and Setup

## Before You Start
- **API Key**: Obtain your AssemblyAI API key from [dashboard link]
- **Audio Requirements**: Supported formats (MP3, WAV, etc.), file size limits
- **Transcript Dependency**: Audio must be transcribed first using AssemblyAI's transcription service
- **Pricing**: Token-based pricing model - see [pricing page link]

3. Quick Start Example

# Basic LeMUR workflow example
import requests

# Step 1: Upload and transcribe audio
transcript_response = requests.post(
    "https://api.assemblyai.com/v2/transcript",
    headers={"authorization": "YOUR_API_KEY"},
    json={"audio_url": "https://example.com/audio.mp3"}
)

transcript_id = transcript_response.json()["id"]

# Step 2: Apply LeMUR to the transcript
lemur_response = requests.post(
    "https://api.assemblyai.com/lemur/v3/generate/task",
    headers={"authorization": "YOUR_API_KEY"},
    json={
        "transcript_ids": [transcript_id],
        "prompt": "Summarize the key points from this conversation"
    }
)

Structural Improvements

1. Add Navigation Hierarchy

## Getting Started (NEW SECTION)
1. [Understanding LeMUR Basics](lemur-basics)
2. [Your First LeMUR Request](quickstart)
3. [Authentication & Setup](setup)

## Common Workflows
[Current "Basic LeMUR Workflows" section - renamed for clarity]

2. Improve Section Descriptions

Instead of just link lists, add context:

## Basic LeMUR Workflows
Start with these fundamental patterns that demonstrate core LeMUR capabilities:

- **[Setup an AI Coach with LeMUR](task-endpoint-ai-coach)** - Build an AI assistant that provides feedback on conversations
- **[Generate Action Items with LeMUR](task-endpoint-action-items)** - Automatically extract tasks and next steps from meetings

Content Clarity Issues

1. Ambiguous Terminology

• Replace “Apply Large Language Models to spoken data” with more specific language
• Define the relationship between transcripts and LeMUR processing
• Clarify what “framework” means in this context

2. Missing Context for Sections

## Analyze Speakers with LeMUR
Use LeMUR to understand who said what and identify speakers in your audio:

[Current links with brief descriptions]

## Get Quotes and Citations with LeMUR
Extract specific quotes and their timestamps for verification and reference:

[Current links with descriptions]

User Experience Pain Points

1. No Clear Entry Point

Problem: Users don’t know where to start Solution: Add a prominent “New to LeMUR?” section with a clear learning path

2. Overwhelming Link Lists

Problem: 20+ links with similar titles create decision paralysis Solution:

## Recommended Learning Path
1. **Start Here**: [LeMUR Basics] → [Quick Start] → [Your First Summary]
2. **Common Use Cases**: [Meeting Notes] → [Action Items] → [Q&A]
3. **Advanced Features**: [Speaker Analysis] → [Citations] → [Custom Workflows]

3. Missing Difficulty Indicators

Add complexity indicators:

- 🟢 **[Generate Action Items](link)** - Beginner - Extract tasks from meetings
- 🟡 **[Speaker Identification](link)** - Intermediate - Requires speaker diarization
- 🔴 **[SOAP Note Generation](link)** - Advanced - Medical domain expertise needed

Additional Recommendations

1. Add Visual Elements

• Include a workflow diagram showing: Audio → Transcript → LeMUR → Results
• Add screenshots of typical outputs
• Create comparison tables for different LeMUR endpoints

2. Cross-Reference Related Docs

## Related Documentation
- [Audio Transcription Guide](../transcription/) - Required first step
- [API Authentication](../auth/) - Setup requirements
- [Pricing Calculator](../pricing/) - Estimate costs

3. Include Troubleshooting Section

## Common Issues
- **"Transcript not found"** - Ensure transcription is complete before LeMUR requests
- **High token costs** - See [token estimation guide](counting-tokens)
- **Poor results** - Review [prompt engineering best practices](prompt-tips)

This analysis addresses the main gaps in helping users understand what LeMUR is, how to get started, and how to navigate the extensive feature set effectively.

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