Feedback: guides-past-response-prompts

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/past-response-prompts
Category: guides
Generated: 05/08/2025, 4:38:51 pm

---

Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:38:50 pm

Technical Documentation Analysis: Pass Context from Previous LeMUR Requests

Overall Assessment

This documentation provides a functional guide but has several significant gaps that could frustrate users and limit adoption. The core concept is clear, but execution lacks depth and polish expected for developer documentation.

Critical Issues & Recommendations

1. Missing Essential Information

Problem: Several key technical details are absent or unclear.

Specific Issues:

• No error handling examples or common error scenarios
• Missing information about token limits and cost implications
• No explanation of context window limitations
• Undefined behavior when context becomes too large

Recommendations:

# Add error handling example
try:
    result = transcript.lemur.task(initial_prompt)
    if len(result.response) == 0:
        print("Warning: Empty response received")
except assemblyai.LemurError as e:
    print(f"LeMUR API error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Add a section on Token Management:

• Explain approximate token costs
• Show how to estimate context size
• Provide context truncation strategies

2. Structural Improvements

Current Issues:

• Quickstart section duplicates Getting Started content
• No clear learning progression
• Missing prerequisites section

Recommended Structure:

# Pass Context from Previous LeMUR Requests

## Overview
[Brief explanation + use cases]

## Prerequisites
- AssemblyAI account and API key
- Python 3.7+
- Basic familiarity with LeMUR

## Quick Example
[Minimal working example - 10 lines max]

## Step-by-Step Implementation
[Current "Getting Started" content, improved]

## Advanced Usage
[Context management, error handling, optimization]

## Troubleshooting
[Common issues and solutions]

## Related Resources
[Links to other relevant docs]

3. Code Quality Issues

Problems:

• Inconsistent model usage (Haiku recommended, but Sonnet used in code)
• Magic strings and hardcoded values
• No input validation

Improved Code Example:

import assemblyai
from typing import List

class LeMURConversation:
    def __init__(self, api_key: str, model: assemblyai.LemurModel = assemblyai.LemurModel.claude3_haiku):
        assemblyai.settings.api_key = api_key
        self.transcriber = assemblyai.Transcriber()
        self.model = model
        self.context_history: List[str] = []
        self.max_context_length = 4000  # tokens

    def transcribe_audio(self, audio_source: str):
        """Transcribe audio file or URL"""
        try:
            return self.transcriber.transcribe(audio_source)
        except Exception as e:
            raise Exception(f"Transcription failed: {e}")

    def ask_question(self, prompt: str, transcript) -> str:
        """Ask a question with context from previous responses"""
        # Manage context size
        context = self._get_managed_context()

        if context:
            full_prompt = f"{prompt} (Previous context: {context})"
        else:
            full_prompt = prompt

        try:
            result = transcript.lemur.task(full_prompt, final_model=self.model)
            self.context_history.append(result.response)
            return result.response
        except Exception as e:
            raise Exception(f"LeMUR request failed: {e}")

    def _get_managed_context(self) -> str:
        """Get context while managing token limits"""
        context = " ".join(self.context_history)
        # Simple truncation - could be more sophisticated
        if len(context) > self.max_context_length:
            context = context[-self.max_context_length:]
        return context

4. User Experience Pain Points

Current Issues:

• No guidance on effective prompt writing
• Missing examples of good vs. poor follow-up questions
• No explanation of when this approach is most useful

Recommendations:

Add Best Practices Section:

## Best Practices

### Effective Follow-up Questions
✅ **Good**: "What was Jenna's recovery timeline?"
❌ **Poor**: "Tell me everything about her"

### When to Use Context Passing
- **Ideal for**: Character development, multi-step analysis, iterative refinement
- **Not ideal for**: Simple one-off questions, completely unrelated queries

### Managing Conversation Flow
- Keep individual responses focused
- Periodically summarize to compress context
- Reset context for new topics

5. Missing Examples and Use Cases

Add Multiple Scenarios:

# Example 1: Meeting Analysis
initial_prompt = "Summarize the key decisions made in this meeting"
follow_up = "Who was responsible for each decision?"

# Example 2: Content Analysis
initial_prompt = "What are the main themes in this podcast?"
follow_up = "Which theme appeared most frequently?"

# Example 3: Educational Content
initial_prompt = "Explain the medical concepts discussed"
follow_up = "Which concept would be hardest for a beginner to understand?"

6. Technical Accuracy Issues

Problems:

• Inconsistent model recommendations
• No mention of rate limits
• Missing API version compatibility

Fixes:

• Align model recommendations throughout
• Add rate limiting guidance
• Specify SDK version compatibility
• Include links to API reference documentation

Priority Implementation Order

1. High Priority: Add error handling and input validation
2. High Priority: Fix model consistency issues
3. Medium Priority: Restructure content organization
4. Medium Priority: Add context management strategies
5. Low Priority: Expand examples and use cases

Additional Recommendations

• Add a troubleshooting section with common errors
• Include performance tips for large conversations
• Provide cost estimation guidance
• Add links to related documentation (LeMUR basics, model selection)
• Consider adding a video walkthrough or interactive demo

This documentation has good bones but needs significant enhancement to meet professional developer documentation standards. The improvements above would transform it from a basic code example into a comprehensive, user-friendly guide.

---