Putting to one side the early “TI uses VBA” misstep example in the previous blog post, ChatGPT can, in fact, help explain what a TI script is doing. If you ask ChatGPT to explain “What does this Turbo Integrator script do?” and paste the combined Prolog, Metadata, Data, and Prolog TI scripts from the “Planning Sample” TI process “plan_load_budget_ascii”, ChatGPT will quickly describe the script:
Bear in mind that there are over 200 lines of TI script that I submitted to ChatGPT here! ChatGPT’s description breaks the script down into logical sections of work that ChatGPT has identified and describes each in some detail. It concludes with a pretty accurate summary of what the script is doing: loading data to the “plan_BudgetPlan” cube, with logging disabled during the data load.
And here’s where ChatGPT shines: you can ask it follow-up questions and drill into even more detail about specific parts of what the script is doing, for instance:
It is, frankly, very impressive that ChatGPT can understand this much about TI script and what some of the TI script functions are designed to do, like CUBESETLOGCHANGES. It does this without having access to any descriptive comments in the TI process’ script (this is Planning Sample, after all). ChatGPT is clearly able to understand things like:
A word of caution about ChatGPT and your data!
Please do not include any sensitive data (TI scripts, PA data, etc.) if you are using the ChatGPT website (https://chat.openai.com/). Any information submitted via ChatGPT’s website has no guarantees of privacy and could be used to train ChatGPT, which could, in theory, mean your data ends up being provided as an answer to someone else’s query! If you need to use ChatGPT with data like this then you should look at ChatGPT API’s, which do offer data privacy protections.
It seems clear that ChatGPT, as shown in the examples above, can potentially help generate some basic documentation about existing TI processes today. Documentation is one of those jobs nobody enjoys doing, especially when you are documenting TI processes that you may not have written. You may need to edit some of ChatGPT’s responses or ask it to go into more detail on some sections but using AI like ChatGPT in this way could truly be a timesaver here!
Unlike many of us who have been PA consultants for years, ChatGPT does not immediately volunteer its opinion on whether the TI process appears to be badly written. 😊 But if ChatGPT can understand TI script, can it also make suggestions for improving that script? This is the point where the story gets a little disappointing (at least as of late March 2023). I asked ChatGPT this sort of question and got a long list of suggestions:
The “Planning Sample” model does, indeed, leave a lot to be desired from a design perspective, but most of the suggestions in this response will not optimize this TI script. Let us focus on two of the problems in this response:
What you are seeing here from ChatGPT, like in the first post in this series, are examples of the phenomenon of AI “hallucinations.” Clearly there are some things ChatGPT still needs to learn about TI scripts and TI script optimization. And like we at QueBIT often say about the best consultants: ChatGPT also needs to do a better job knowing what it doesn’t know!
In the final post in this series on ChatGPT and TI scripts, I will be looking to see if ChatGPT can generate TI script for us. If you are interested in learning about some of the tools discussed here or how to leverage other kinds of AI and automation tools, including ChatGPT API’s, in your business do not hesitate to contact us!