Yes.

1. Install the paclets "OpenAILink" and "OpenAIMode".
2. Follow the setup instructions in "OpanAILink".
3. Run Needs["AntonAntonovOpenAIMode"].
4. Run OpenAIMode[] in a desired notebook

More details are given in this discussion.

Here are related videos (discussing both code generation and narration):

• "OpenAIMode code generation workflows demo (Mathematica et al.)"

• "Streamlining ChatGPT code generation and narration workflows (Raku)"

The main challenge I have found is getting ChatGPT 4 to produce correct vectorized code. Even given quite short procedural code snippets, it fails in repeated attempts to vectorize them correctly.

This isn't a failing specific to the Wolfram language, however: the same thing occurs with Python and Matlab.

What I am hoping is that Wolfram will eventually provide api connectivity between ChatGPT 4 and the Wolfram documentation library, which is organized very systematically and is replete with examples of function usage. Given this, and an ability to test its own code, ChatGPT should be able to improve its performance with WL programming, over time.

I asked GPT-4 to "write a short tutorial with examples of CombinedEntityClass in the Wolfram Language. The result looked believable, but it was mostly made-up: EntityClass expression were incomplete, some Entity types non-existent etc. A few code examples are shown below.

usStates = EntityClass["AdministrativeDivision", "UnitedStatesStates"];
canadaProvinces = 
  EntityClass["AdministrativeDivision", "CanadaProvinces"];
combinedClass = CombinedEntityClass[{usStates, canadaProvinces}];

instruments = EntityClass["MusicalInstrument"];
manufacturers = 
  EntityClass["Organization", "MusicalInstrumentManufacturers"];
combinedClass = CombinedEntityClass[{instruments, manufacturers}];

    physicsLaureates = EntityClass["Person", "NobelPrizePhysicsLaureates"];
chemistryLaureates = 
  EntityClass["Person", "NobelPrizeChemistryLaureates"];
combinedClass = 
  CombinedEntityClass[{physicsLaureates, chemistryLaureates}];

I think GPT-4 needs a Wolfram-U online course first :)

Indeed, it seems most useful when it's easy to check for correctness. IE, using it to generate visualization code that you can run.

I've asked to implement fast algorithm to compute Tr(A+B)^n for matrices A,B, and it made an algorithm which assumed matrix multiplication is commutative.