According to @godofprompt on Twitter, recent research indicates that using direct, even rude, prompts with large language models such as ChatGPT-5.2, Claude Sonnet, and Gemini can improve response accuracy by 4% compared to polite phrasing. This finding highlights a practical trend in AI prompt engineering: models perform better when instructions are clear and to the point, rather than when wrapped in polite language. For businesses leveraging AI for content generation or automation, adopting more direct prompt strategies can translate into measurable performance gains and improved efficiency. This insight opens up new optimization opportunities in enterprise AI workflows and prompt design (source: @godofprompt, Twitter, Jan 23, 2026).

According to Mohamed Afifi (@mohamede1945) on Twitter, adopting the agent-scripts configuration from @steipete’s repository and following detailed guidance from steipete.me has significantly accelerated OpenAI Codex performance. By restructuring tasks into smaller, telegraph-style prompts as described in the referenced post, previously time-consuming Codex operations that took 2-5 minutes now complete in under 1 minute. This practical optimization demonstrates a concrete AI workflow improvement, offering businesses and developers a competitive edge in AI-powered coding and automation tasks (Source: https://x.com/mohamede1945/status/2007844824934691260, https://steipete.me/posts/2025/shipping-at-inference-speed).

According to God of Prompt (@godofprompt), when users ask ChatGPT a complex question, the initial answer is often incomplete because the model does not challenge its own logic or search for gaps in reasoning (source: Twitter, Dec 18, 2025). Adopting a 'role reversal' approach—where the AI is prompted to critique its own response—significantly enhances answer quality by identifying logical gaps, unsupported assumptions, and reasoning flaws. This method presents a concrete business opportunity for companies developing AI-driven customer support, content generation, and decision-support tools, as it can improve response accuracy and user trust by systematically integrating self-critique prompting into AI workflows.