I work for a firm that develops custom software in regulated industries, and we have brilliant software & data engineers in their 20's working on compliance auditing, and more specifically "Compliance Management System health monitoring."
We've be able to use a lot of AI-assisted engineering and AI in the software to solve longstanding business challenges in this space.
I won't make assumptions about where you're located, but on the East Coast US it is big business among banks, utilities, healthcare, etc.
Great read. I have fond memories of eagerly awaiting the launch of Mindstorms, having gotten to play with LEGO LOGO kits that my dad was able to bring home from the school he worked at.
I appreciate that LEGO has always leaned into programmable tech for consumers.
Also, shoutout to any OG rec.toys.lego group members if any of y'all are reading.
Thank you for sharing this. I’ve been working on something for editing exiting code by using GitHub Issues and Actions for prompting, with the response from GPT as a Pull Request/Comment on the Issue[1]. I will definitely try out your project!
I’ve been experimenting with sliding window chunking using SRT files. They’re the subtitle format for television and have 1 to _n_ sequence numbers for each chunk, along with time stamps for when the chunk should appear on the screen. Traditionally it’s two lines of text per chunk but you can make chunks of other line counts and sizes. Much of my work with this has been with SRT files that are transcriptions exported from Otter.ai; GPT-3.5 & 4 natively understand the SRT format and the concepts of the sequence numbers and time stamps, so you can refer to them or ask for confirmation of them in a prompt.
"But did you try with GPT4?" is becoming a bit of a refrain for me when talking about this with people. I can get GPT-4 to produce working code from agile user stories surrounded by a well-crafted prompt.
Great use case. I have successfully used it in a similar way with SRT files (recording transcripts in SubRip Subtitle format) as well as CSV data from surveys that often has many columns with long text labels (the survey questions) and free text answers.
Ex. a prompt I had used for a developer skills survey .csv was:
> The CSV data below is the results of a skills survey sent to a group of software engineers. The first row is a header row. Please summarize this data in the areas that the People are Strong, Weak, Most Similar, and Unique:
Then, because of things I saw in the response, I asked a few follow-up questions:
> How much Azure experience is there in the group?
> Can you provide more explanation around your assessment that "Engineers generally have little to no experience with Dockers and Kubernetes."
> What other skills and experience do you see in the results that you haven't already mentioned?
To address my risk tolerance vis-a-vis the ChatGPT warnings (and previous UI leak of responses), I replaced the email addresses in the .csv file with "PersonA", "PersonB", ...
We've be able to use a lot of AI-assisted engineering and AI in the software to solve longstanding business challenges in this space.
I won't make assumptions about where you're located, but on the East Coast US it is big business among banks, utilities, healthcare, etc.