Title: ChatGPT & The Chicken Soup Problem: A Deep Dive into AI Models
???? When exploring AI, particularly generative models like ChatGPT, it’s crucial to understand what I refer to as the "Chicken Soup Problem." This metaphor illuminates the distinctions between deterministic and generative AI models, highlighting their applications and limitations.
?? Deterministic Models: Deterministic AI adheres to a predefined set of rules and algorithms, delivering consistent outputs for identical inputs. For example, a Google search for "chicken soup recipes" repeatedly yields the same top recipes. These models are designed to sift meticulously through data, ensuring the results are not only relevant but also stable, predictable, and reliable. ?We can then choose the optimal chicken soup recipe by evaluating the details about its source, complemented by user reviews and ratings.
?? Generative Models: In contrast, generative models like ChatGPT create responses anew with each request. Asking for a chicken soup recipe will prompt ChatGPT to generate one immediately. It doesn't retrieve a recipe; it generates a new one each time based on its understanding of chicken, soups, and cooking methods.
Understanding this concept is essential: Generative AI is not always the best option for delivering accurate information or the finest chicken soup recipe. Providing a mediocre recipe could easily lead to customer dissatisfaction and harm your reputation for a business.
In a recent course I took, Andrew Ng suggested we may be better to think about large language models (LLMs) as robust reasoning engines rather than full-fledged databases. Just like people, LLMs can produce incorrect answers if they lack the correct information. They need to be given the right sources of information to be effective.
?? Takeaway: As we delve deeper, the diverse roles and applications of generative AI for businesses are becoming clearer. The "Chicken Soup Problem" underscores a key limitation of generative AI, emphasizing the importance of recognizing its boundaries. Their true strength lies in reasoning." The strategic integration of different AI types, enhanced by proprietary data, is where businesses will find immense value. Innovations such as Retrieval-Augmented Generation (RAG) and chain of thought reasoning, agentic workflows are pioneering this integrated approach, heralding a new era of AI utility in business. But without robust verification processes from validated data sources, you will be putting your reputation at risk. No one likes bad chicken soup.