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Generative Solutions vs. Scripted ones
AI Startups' Dilemma: Choosing Between Scripted and Generative Solutions
The main difference between scripted and generative solutions lies in the way responses are generated in AI applications:
Scripted solutions utilize predefined prompts or a set of pre-prepared prompts for specific situations or queries. These solutions are deemed more reliable and constrained because they adhere to a fixed set of choices. However, they can be less creative and human-like since they can only present results based on predetermined content. These are frequently used in contexts requiring strict frameworks, such as medicine or teaching.
On the other hand, generative solutions can craft new and unique answers based on the dynamic data and the context provided. They employ machine learning algorithms, particularly deep learning and transformer models, to process input data or even generate outputs that have yet to be pre-programmed. Generative solutions are often viewed as riskier due to the possibility of unexpected results. Still, they are more creative, engaging, and human-like, as they can adapt and improvise during user interactions.
AI sector startups are compelled to find an optimal balance between these two approaches, considering their target market and niche. When opting for generative solutions, they must also include security measures and risk management strategies.
I engage in much experimentation, specifically with generative inputs or prompts. Interestingly, the AI model communicates with itself (or, rather, with its other entity). This process is truly fascinating.
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