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.

OpenRules is Shining in the Serverless World

When 3.5 years ago we introduced a new OpenRules Decision Manager, it was specifically designed as a Decision Intelligence Framework for creation, debugging, and management of Superfast Decision Microservices for that time brand new Serverless world. Over the last 3 years we witnessed how major corporate customers migrated their rules-based applications deployed on the large web servers to OpenRules. Over the last few weeks we saw how several new customers were really surprised that they don’t need anymore heavy lifting for building and managing their rules-based light-weighted microservices. In this brief post I share a working sample that demonstrates how easy it is to build, test, debug, deploy, and run RESTful decision services with OpenRules using any on-cloud or on-premise infrastructure.

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