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“instead of doing LLM pingpong where you're going back and forth”
What It Means
Users waste time manually testing the same prompt across multiple AI platforms to find the best response
Why It Matters
Identifies a clear inefficiency in current AI workflows that creates market opportunity
When It's True
When users need high-quality outputs and typically compare multiple AI platforms for the same task
When It's Risky
When specific AI models are required for certain tasks or when quality differences are minimal
How to Apply
Identify workflows where users manually compare AI outputs
Build aggregation tools that eliminate repetitive testing
Focus on reflection/analysis to automatically select best results
Example Scenario
“Content creator testing blog post intro on ChatGPT, then Claude, then Perplexity to find best version - multi-agent system does this automatically in single prompt”
Related Knowledge
Elimination of Manual LLM Comparison Workflows
Users are moving from manually testing prompts across multiple AI platforms (ChatGPT, Claude, Perplexity) to unified systems that query multiple models simultaneously.
Structured AI prompting techniques becoming essential business skill
The gap between basic AI users getting 'AI slop' and advanced users getting 10× results is widening.