According to @godofprompt, the caveman plugin hit 51k+ GitHub stars in 2 weeks and cuts Claude output tokens by 65%, enabling cheaper, faster replies.

According to @godofprompt, classic communication theories from 1948 directly improve prompt writing and outperform most prompt engineering hacks.

According to @godofprompt, a viral “5th horse” prompt tricked ChatGPT, highlighting model perception gaps and prompt‑robustness risks.

According to DeepLearningAI, Andrew Ng shares practical prompting tactics to get specific, creative AI outputs in a new course.

According to DeepLearningAI, Andrew Ng teaches why models over-agree and how better prompts yield accurate, useful answers in a new course.

According to God of Prompt, Grok Skills lets users save custom abilities as My Skills for permanent reuse across writing, research, and coding.

According to @godofprompt, a FAANG Reddit thread shows 7 Claude workflows beating vague prompts by enforcing review, isolation, and ownership.

According to DeepLearningAI, writing specs first keeps coding agents aligned and prevents costly misbuilds.

According to @godofprompt, a Domain Vocabulary prompt helps teams name parts of AI systems to cut ambiguity and ship faster.

According to @gdb, over half of Codex prompts now come from non-engineers, signaling broader productivity gains across knowledge work.

According to Ethan Mollick on X, assigning expert personas like physicist or lawyer barely changes LLM answer accuracy, based on controlled tests.

According to @godofprompt, ubiquitous AI prompting reframes creative work, turning iteration loops into scalable content and workflow gains.

According to DeepLearningAI, a 7-day AI prompting challenge tied to its new course runs with a May 5 deadline, driving practical problem-solving skills.

According to @emollick, frequent AI users spot stock phrases like “load bearing” and “not X, but Y,” revealing telltale AI prose patterns.

According to AndrewYNg, a new course teaches cross-model prompting skills for ChatGPT, Gemini, and Claude to level up productivity and results.

According to DeepLearningAI on Twitter, Andrew Ng’s new AI Prompting for Everyone course is live, teaching techniques for accurate, useful model outputs.

According to @_avichawla on X, the thread outlines eight prompt engineering techniques—beyond zero-shot prompting—to consistently improve large language model outputs for production use. As reported by the tweet, the methods include few-shot prompting for pattern learning, role prompting to set system behavior, step-by-step reasoning prompts, constraint and format specifications, providing reference context, iterative refinement loops, self-critique or reflection prompts, and tool-augmented prompting. According to the original post, these techniques raise response quality, reduce hallucinations, and improve reproducibility across models like GPT4 and Claude3, which is critical for enterprise workflows such as report generation, customer support, and analytics. As cited in the thread, adding examples and explicit schemas can cut post-edit time and increase acceptance rates in business pipelines, offering immediate ROI for teams deploying LLMs in content ops, code assistance, and data extraction.

According to @AnthropicAI on X, experiments found that custom persona instructions for Claude—ranging from a courteous style to an exasperated, down-and-out cowboy—were followed but did not materially improve negotiation outcomes compared with polite defaults (as reported by Anthropic, April 24, 2026). According to Anthropic, this suggests limited performance lift from prompt persona hardening in bargaining tasks, indicating businesses should prioritize structured objectives, constraints, and reward signals over stylistic roleplay for deal-making use cases. As reported by Anthropic, the practical takeaway for enterprise AI deployment is to focus on grounded task design, calibrated utility functions, and tool integration rather than aggressive tones when optimizing LLM negotiation agents.

According to Josh Woodward on X, Gemini now supports conversation branching that lets users spin up a new, separate chat from any point in a thread without losing original context, enabling parallel idea exploration and cleaner workflows for prompt engineering and product research. As reported by Google Gemini on X, the feature is rolling out to 20% of users and ramping up, signaling imminent broad availability for consumer and enterprise accounts. According to the posts, this improves collaboration by letting teams fork prompts for A B testing model responses, compare instructions side by side, and preserve audit trails in regulated settings where traceability of prompt changes matters.

According to God of Prompt on X, the YouMind‑OpenLab repository aggregates an open-source prompt library for GPT‑Image‑2 with curated examples, style templates, and real-world use cases, enabling faster prompt engineering workflows for image generation; as reported by the GitHub project page, the collection standardizes prompt structure, tags, and parameters to improve reproducibility and fine-tuning datasets for downstream vision tasks and marketing creatives. According to the GitHub README, teams can adapt the prompts for batch generation, A/B testing, and dataset bootstrapping, which creates opportunities for agencies, e‑commerce, and game studios to scale content while maintaining brand style control and measurable conversion testing.