Prompt Runner

The Prompt Runner node executes composed prompts through selected language models, serving as the execution engine for prompt-based interactions. It takes assembled prompts and returns model-generated responses.

Key Features

• Executes composed prompts
• Supports multiple language models
• Provides direct model responses
• Integrates with Prompt Composer output

Configuration

Basic Setup

1. Add the Prompt Runner node to your skill
2. Connect the composed_prompt input
3. Select your desired language model

Node Outputs

model_response

• Raw response from the language model
• Format depends on prompt instructions
• Can be used directly or processed further

Best Practices

Prompt Quality

• Ensure incoming composed prompts are clear and complete

Model Selection

• Choose based on task complexity
• Consider performance requirements
• Balance quality vs. speed
• Account for token limits

Common Issues and Solutions

Slow Response Times

• Check prompt length
• Verify model selection
• Monitor system resources
• Consider prompt optimization

Unexpected Responses

• Review prompt structure
• Check input formatting
• Verify example quality
• Test with different models

Use Cases

1. Customer Support Assistant

Implementation:

• Input: Customer inquiry prompts
• Model: GPT-4 for complex understanding
• Output: Formatted support responses

Prompt Composer → Prompt Runner → Response Formatter

2. Data Analysis Reporter

Implementation:

• Input: Data analysis prompts with data frame context
• Model: GPT-4 for analytical tasks
• Output: Structured analysis reports

Data Query → Prompt Composer → Prompt Runner → Report Generator

Integration Tips

With Prompt Composer

1. Connect composed_prompt output directly
2. Verify prompt format
3. Test example matching
4. Monitor response quality

With Output Processors

1. Format model responses
2. Extract key information
3. Structure data as needed
4. Prepare for next steps

With Other Components

1. Ensure data compatibility
2. Manage state properly
3. Handle errors gracefully
4. Monitor performance

Troubleshooting Guide

Response Quality Issues

1. Review prompt quality
2. Check example relevance
3. Verify model selection
4. Test with different prompts

Performance Problems

1. Monitor response times
2. Check system resources
3. Optimize prompt length
4. Consider model alternatives

Integration Issues

1. Verify connections
2. Check data formats
3. Test component flow
4. Monitor error logs

Conclusion

The Prompt Runner node is a crucial component for executing AI-powered interactions. When properly configured and integrated with Prompt Composer, it provides reliable and efficient prompt execution for various use cases.