The only way I've found to back up the claims: try to prove them myself :)
or at least attempt to. I'm fairly convinced CLIPS is an unexplored middle-ground answer to conversations around "does our company really need AI/ML? SQL is enough."
I also see CLIPS as a declarative abstraction around our classic imperative CRUD applications. It exposes abstract concepts within the language that we end up implementing ourselves, like application caches (working memory), pattern matchers (LHS of Rules), and permission systems (in CLIPS, the object-oriented concepts known as "COOL" list User as a separate object inheritance chain from the other objects).
Consider PostgreSQL, a popular database that one could consider a Rules Engine:
1. records stored in tables can be implemented with facts in working memory
2. constraints, foreign keys, views, and other abstractions that come with RDBMSs which we slowly replicate within our application layer over time can be implemented with defrules
3. and you can even add "Rules" (https://www.postgresql.org/docs/current/sql-createrule.html) proper in psql
From this, we could conclude PostgreSQL is an example of an application you can build with a rules engine. However, the ambitions of CLIPS is to be a tool used for creating "Expert Systems." Whenever I bring up that term with other people, I'm met with:
1. blank stares
2. scoffs
regardless of their profession.
However, I argue that ChatGPT and other AI/ML chat bots are highly advanced "jack of all trade" Expert Systems. I also argue that some very successful web applications, such as the Pennie Pennsylvania health insurance application and TurboTax are "specific" Expert Systems. In all of these systems, you interact with someone who is the "expert" and can carry a "conversation" on the topic you specify.
We already use imperative programming languages that query remote databases specifically written to store data. I think Rules Engines (CLIPS) is "low hanging fruit" because, at its core, it's "lower level" than an RDBMS in terms of abstractions provided to the developer, but the implementation of the algorithm that interprets CLIPS code is closer inline with AI/ML. ie: neural networks are built based on inferred rules from the data they're trained on. Rete networks are built based on explicitly defined Rules by the developer. Thus, CLIPS is sort-of like having a formal language for interacting with a simplified neural network.
I hope my efforts are reaching developers on teams who have reached the point in their product's life when CLIPS "would have been a good idea to start with."
The difficult part: forgoing the temptation to reason "we don't need to use Foo, our product doesn't need all of that."
If any of the above resonates with you, I encourage you to read the CLIPS documentation and try to build something fun with it. You might be surprised at what you learn.