New systems employ Machine Learning to sift through large knowledge sources, creating flexible Large Language Models. These models discern context and predict sequential information in various communication forms. Generative AI, leveraging Transformers, generates textual or visual outputs mimicking human responses. It proposes one or multiple contextually feasible solutions for a user to contemplate. However, generative AI does not currently support traceability of ideas, a useful feature provided by search engines indicating origin of information. The narrative style of generative AI has gained positive reception. People learn from stories. Yet, early ChatGPT efforts had difficulty with truth, reference, calculations, and aspects like accurate maps. Current capabilities of referencing locations and linking to apps seem to be better catered by the link-centric search methods we've used for two decades. Deploying truly believable solutions extends beyond simulating contextual relevance as done by generative AI. Combining the creativity of generative AI with the provenance of internet sources in hybrid scenarios could enhance internet usage. Generative AI, viewed as drafts, stimulates thinking, offering alternative ideas for final versions or actions. Scenarios for information requests are considered. We discuss how generative AI can boost idea generation by eliminating human bias. We also describe how search can verify facts, logic, and context. The user evaluates these generated ideas for selection and usage. This paper introduces a system for knowledge workers, Generate And Search Test, enabling individuals to efficiently create solutions previously requiring top collaborations of experts.

翻译：新系统利用机器学习筛选海量知识源，构建灵活的大型语言模型。这些模型能辨识语境并预测不同交流形式中的序列信息。基于Transformer的生成式AI可生成模仿人类回应的文本或视觉输出，为用户提供一种或多种语境可行的解决方案。然而，当前生成式AI不支持思想溯源——这一搜索引擎提供的信息来源标注功能极具实用价值。生成式AI的叙事风格广受好评，人类确实善于从故事中学习。但早期ChatGPT在事实准确性、引用、计算及精确地图等领域存在困难。如今定位引用与应用链接能力，似乎更适用于我们沿用二十年的链接中心搜索方法。要部署真正可信的解决方案，需超越生成式AI模拟语境关联性的边界。在混合场景中融合生成式AI的创造力与互联网资源可追溯性，有望提升互联网使用体验。将生成式AI视为思维草稿，可激发灵感，为最终版本或行动方案提供替代思路。本文探讨了信息请求的多种场景，论述了生成式AI如何通过消除人类偏见促进思想生成，并阐释了搜索技术如何验证事实、逻辑与语境。用户最终评估这些生成思想以便筛选应用。本文为知识工作者提出"生成与搜索测试"系统，使个人能高效创建以往需顶级专家团队协作才能完成的解决方案。