▲ | ultimateking 4 days ago | |||||||
I went through the structure and found the semantic correction idea pretty intriguing. Can you explain a bit more about how WFGY actually achieves such improvements in reasoning and stability? Specifically, what makes it different from just engineering better prompts or using more advanced LLMs? | ||||||||
▲ | WFGY 4 days ago | parent [-] | |||||||
Great question—and I totally get the skepticism. WFGY isn’t just another prompt hack, and it’s definitely not about making the prompts longer or more “creative.” Here’s the real trick:
So, the big difference: WFGY makes “meaning” and logical repair part of the prompt process itself—not just hoping for the model to “guess right.”
If you’re curious about specific edge cases or want to try it on your own workflow, happy to walk you through! | ||||||||
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