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Multi-Agent LLM Workflow
A system that simultaneously queries multiple Large Language Models (LLMs) for the same prompt and uses reflection to select the best response, eliminating manual comparison across different AI platforms.
How It Works
Instead of manually testing prompts across ChatGPT, Claude, Perplexity, etc., the system sends one prompt to multiple models simultaneously, analyzes outputs using reflection, and presents the optimal result.
Components
Submit single prompt to interface
System distributes prompt to multiple LLMs (GPT-4, Claude, Gemini, etc.)
Each model generates independent response
Reflection engine analyzes and compares outputs
System presents best response based on reflection analysis
User can view alternative responses if needed
When to Use
When you need high-quality outputs and typically compare responses across multiple LLMs, for creative content, complex analysis, or when output quality is critical.
When Not to Use
For simple queries where any LLM would suffice, when you have specific model preferences for certain tasks, or when cost optimization for single queries matters more than quality.
Anti-Patterns to Avoid
Example
“Creating a YouTube video intro: instead of testing the same prompt on ChatGPT, then Claude, then Perplexity manually, submit once to multi-agent system which tests all models and returns the most engaging, on-brand intro script.”