Tutorial: Qlik Sense Customization

Anyone who has brewed coffee with freshly grinded coffee beans using a french press knows that instant coffee doesn’t even come close. The flavor and aroma are unmatched. Generative AI output works the same way. What the best ingredients and equipment is to a morning cuppa, prompt engineering is to better generative AI output.

As the ongoing debate surrounding the value of generative AI unfolds across various industries, there's an undeniable momentum as leaders boldly invest in this transformative technology.

A recent survey by Gartner showed that nearly 68% of executives at top organizations believe that the benefits of generative AI far outweigh the current risk proposition.

In recent years, an ever-growing number of organizations have been relying on data analytics to drive their decision-making processes, and understandably so. With a robust analytics framework in place, they can leverage their available data and make informed decisions.

Today, artificial intelligence and machine learning have made rapid inroads into mainstream consumer technology across all sectors. From self-driving cars to investment advisory services, we are discovering new use cases almost daily. But over the past couple of months, the loudest voice and perhaps the biggest mainstream AI breakthroughs have been centered around large language model research. In simple terms, the use of natural language-enabled AI systems is bringing machines and humans closer as interactions between the two become more natural.