According to @godofprompt, a key trend in prompt engineering is Multi-Shot with Failure Cases, where AI engineers provide models with both good and bad examples, along with explicit explanations of why certain outputs fail. This technique establishes clearer output boundaries and improves model reliability for technical applications, such as explaining API rate limiting. By systematically demonstrating what not to do, businesses can reduce model hallucinations and ensure higher quality, more predictable outputs for enterprise AI deployments (source: @godofprompt, Dec 10, 2025). This approach is gaining traction among AI professionals seeking to deliver robust, production-ready generative AI solutions.

According to Andrej Karpathy (@karpathy), there is an ongoing discussion in the AI community about defining 'slop'—a qualitative sense of low-quality or imprecise language model output—in a quantitative and measurable way. Karpathy suggests that while experts might intuitively estimate a 'slop index,' a standardized metric is lacking. He mentions potential approaches involving LLM miniseries and token budgets, reflecting a need for practical measurement tools. This trend highlights a significant business opportunity for AI companies to develop robust 'slop' quantification frameworks, which could enhance model evaluation, improve content filtering, and drive adoption in enterprise settings where output reliability is critical (Source: @karpathy, Twitter, Nov 22, 2025).