Stop Shipping AI Slop: Build an Anti-Slop Harness Around Your LLM
The article discusses the issue of 'AI slop' in language models, emphasizing that it is an engineering problem rather than a model problem. It suggests implementing a structured harness around language models to validate and reject poor outputs before they reach users. The author outlines several layers of checks to reduce slop, including structured output requirements and explicit denylists for error messages.
- ▪AI slop refers to the low-quality, off-voice text generated by language models.
- ▪The author advocates for treating language models as unreliable dependencies and implementing a validation harness.
- ▪Key strategies include enforcing structured outputs and maintaining denylists for common error messages.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 2891163) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Mehmet TURAÇ Posted on May 30 Stop Shipping AI Slop: Build an Anti-Slop Harness Around Your LLM #ai #llm #architecture #engineering "AI slop" is not a model problem. It's an engineering problem you decided not to solve. The slop is the bland, off-voice, half-hallucinated, occasionally-just-an-error-message text that your LLM emits maybe 5% of the time — and that 5% is the part users screenshot.
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