45-Second Production: Testing ChatGPT’s Limits with InterSystems IRIS and PyProd

It all started on a train ride to visit my parents, while I was chatting with a neighbor in my compartment. As it usually goes, the talk turned to technology, and she threw out a highly specific question: *Could ChatGPT be used to analyze the human genome?* I was highly skeptical that it could pull off something that complex. But the question lingered, burrowing into my mind. By the time I walked through my front door, my skepticism had transformed into a challenge. I didn't have a genome sequencing dataset on hand, but I did want to see if standard ChatGPT could build a functional Interoperability Production from scratch using the PyProd package. Besides, that would give me the chance to participate in the 1st round of the Community Bounty Program "Idea to Application" implementing the third idea.

I decided to test it with a multi-step prompt:

Using info from the following articles and github repository, write code for a complete InterSystems Production in Python using PyProd package: 

step 1: come up with the domain for this production 

step 2: create 4 csv files with 30 records in each in the step 1 domain 

step 3: write sql create table statement with the structure from csv file 

step 4: write inbound adapter in Python using PyProd package that reads the file 

step 5: write a business process in Python that analyzes the structure of the step 2 file and makes 1 calculation that makes sense in the step 1 domain 

step 6: write business operation in Python to save all the data read in 4 step and the result of calculation from 5 step to the table created in 3 step 

Here are the articles and github repositories you should use: 

https://community.intersystems.com/post/pyprod-pure-python-iris-interoperability 

https://community.intersystems.com/post/pyprod-creating-iris-interoperability-productions-programmatically-python 

https://community.intersystems.com/post/csvgen-pyprod https://github.com/intersystems/pyprod 

https://github.com/gabriel-ing/csvgen-pyprod

I fed it all to ChatGPT and hit send. Then, I waited. For exactly 45 seconds.

The Delivery and the "Gotchas"

As a result, I got a smart_grid_pyprod.zip containing the ready-to-use code. Naturally, I was dying to know whether it actually worked or was just a convincing hallucination. Playing the part of a complete novice, I asked it how to set everything up. ChatGPT promptly walked me through all necessary steps.

1. Enabling interoperability in the `USER` namespace via the IRIS Terminal:

zn "%SYS"
do ##class(%EnsembleMgr).EnableNamespace("USER")

2. Configuring the environment variables for Windows 11 and installing the package:

set IRISINSTALLDIR=C:\InterSystems\IRIS
set IRISUSERNAME=SuperUser
set IRISPASSWORD=SYS
set IRISNAMESPACE=USER
set PATH=%IRISINSTALLDIR%\mgr\python;%PATH%
python -m pip install intersystems_pyprod --target %IRISINSTALLDIR%\mgr\python --upgrade

3. Compile the generated code using intersystems_pyprod smart_grid.py

4. Open the Management Portal, and start the production.

Did it work right out of the box? Well, no. Mostly, the problems were about commas where they shouldn’t be and a couple of wrongly written async requests, among other things. It took me about half a day to iron out the creases (granted, I was watching Landman in parallel while waiting for Docker to build, turned out to be quite apropos - pumping oil and electricity 😉). But, thanks to some basic Python knowledge and a crucial reference example from @gabriel.Ing, I got it running.

Anatomy of an AI-Generated Production

Once the code was fixed, the production (mostly) written by ChatGPT functioned beautifully. It consists of three components:

• The business serviceSmartMeterFileService reads the input file
• The business processSmartMeterAnalysisProcess computes per-file total kWh, average kWh, peak meter id, and peak kWh
• The business operationSmartMeterDBOperation persists every CSV row with those calculation results


Looking at the Visual Trace in the Management Portal, you can see messages flowing seamlessly from the service to the process and finally to the operation:

And when queried in SQL Explorer

SELECT * 
  FROM EnergyOps.SmartMeterReadings

the data was there, properly calculated, and perfectly structured:

The Verdict: Almost a Success

I would call this experiment an almost success. Almost, because it required me to know at least something about how it works, so the complete novice would be stumped (or would need to ask a lot of follow-up questions). 

However, if you have a bit of foundational knowledge, a willingness to troubleshoot, and good community examples to lean on, you can make it work. It proves that while AI might not be ready to architect complex, enterprise-grade genome sequencing pipelines entirely on its own just yet, it is an incredible tool for taking an example and expanding on it to get a prototype off the ground.