According to @godofprompt on Twitter, a detailed review of OpenAI's model cards, Anthropic's constitutional AI papers, and leaked internal prompt libraries uncovers the real prompting frameworks used by leading AI labs. Unlike generic advice often circulated online, this analysis provides actionable techniques for transforming vague user inputs into precise and structured outputs. As reported by @godofprompt, these frameworks reveal practical approaches for AI practitioners and businesses seeking to optimize large language models for real-world applications.

According to God of Prompt, Anthropic's Constitutional AI leverages negative prompting—explicitly defining what not to include in AI responses—to enhance output quality, with internal benchmarks showing a 34% improvement. This approach involves specifying constraints such as avoiding jargon or limiting response length, which leads to more precise and user-aligned AI outputs. As reported by God of Prompt, businesses adopting this framework can expect significant gains in response clarity and relevance, opening new opportunities for effective AI deployment.

According to @godofprompt on Twitter, a thorough review of OpenAI’s model cards, Anthropic’s constitutional AI research, and leaked internal prompt libraries reveals the concrete prompting frameworks top AI labs use to transform vague inputs into structured, high-quality outputs. This analysis exposes practical methods that significantly enhance model performance, according to the original Twitter thread. The findings highlight actionable business opportunities for enterprises seeking to leverage advanced prompt engineering, as reported by @godofprompt.

According to God of Prompt, constitutional AI prompting is a technique where engineers provide guiding principles before giving instructions to the AI model. This method was notably used by Anthropic to train Claude, ensuring the model refuses harmful requests while remaining helpful (source: God of Prompt, Twitter, Dec 16, 2025). The approach involves setting explicit behavioral constraints in the prompt, such as prioritizing accuracy, citing sources, and admitting uncertainty. This strategy improves AI safety, reliability, and compliance for enterprise AI deployments, and opens business opportunities for companies seeking robust, trustworthy AI solutions in regulated industries.