  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.
