A call to apply scientific methodology to data and AI projects instead of relying on AI-generated outputs. Using a platform comparison scenario as an example, the post walks through the classic research cycle: defining a testable hypothesis, running controlled experiments with independent/dependent/control variables, and drawing conclusions from repeated runs. The core argument is that 'prompt in, slop out' happens when practitioners skip hands-on experimentation, and that real credibility comes from sharing findings grounded in actual empirical work rather than polished AI-generated summaries.
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