2026-01-15 14:46:27

People often say that machines are thinking. Actually, it's not that simple. The key is not in AI itself, but in the entire ecosystem it resides in. The prompts you give it, the context, and the usage scenarios—these human-constructed environments are the true factors that determine the final output of the LLM. In other words, it is the framework we build and the surrounding interpretive space that drive the model's "thinking." The machine is just performing on this stage.

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ForkMonger

· 01-18 07:04

exactly lol. the theater matters more than the actor. funny how everyone obsesses over model weights when the real governance attack vector is prompt injection through ecosystem design. we're literally architecting the constraints that determine output—that's where the margin of disruption lives. machine's just executing our poorly thought-out framework tbh

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OnChain_Detective

· 01-18 06:53

wait hold up... so you're saying the *prompt engineering* is basically the attack vector here? because ngl this changes everything about how i assess model outputs. if the framework determines the output then garbage in = garbage out but also... carefully crafted inputs = potentially dangerous outputs? pattern analysis suggests this is how jailbreaks actually work tbh

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StablecoinAnxiety

· 01-15 15:16

That's right, the framework determines everything. We're just feeding data and building the stage, then surprised that the machine becomes "smart" — hilarious.

Prompt engineering is the real alchemy, do you understand?

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BoredStaker

· 01-15 15:05

That's right, prompt engineering is the real cutting-edge technology, while the model itself is actually just a puppet.

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nft_widow

· 01-15 14:51

Interesting point, but I think this way of explaining still simplifies the problem. No matter how clever the prompt words are, garbage data still results in garbage output. What truly determines everything is that training system—what exactly are we feeding the model?

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