Helping ChatGPT to Build a Working Decision Model

These days only lazy people don’t write about ChatGPT and large language models (LLM). Vendors are trying to be the first to announce a ChatGPT integration even when they don’t have anything serious to show. I’ve also written about it: see “ChatGPT Producing Simple Decision Models and “LLM and Decision Modeling“. This weekend I decided to help ChatGPT (that is now at GPT-4) to address the Challenge “Permit Eligibility” published by It has a simple rule: “An applicant is eligible for a resident permit if the applicant has lived at an address while married and in that time period, they have shared the same address at least 7 of the last 10 years.” But this rule contains several tricky assumptions – no wonder, DM vendors are not in a hurry to submit a solution.

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LLM and Decision Modeling

ChatGPT has the public excited, but the experts are reserved in their praise. Thinking about a practical application of the Large Language Models (LLM) to decision modeling this quote from LeCun caught my attention:

When we create practical decision models we usually deal with an even more limited “universe”. A decision model “manipulates the state of the decision variables” within a very specific business domain (insurance, loan origination, claims, medical guidelines, etc.) complemented by generic concepts well covered by such relatively small standards as DMN and SBVR. Decision modeling universe is really “limited, discrete, deterministic, and fully observable”.

So, being cautious about the current ChatGPT’s hype, we may be more optimistic about the next breakthrough in Decision Modeling. I suspect the answers of experts to my DecisionCAMP-2022 question “Are our Rule Engines Smart Enough?” would be different today.