Breaking mental coding barriers with LLMs
Today, I stepped a bit beyond my comfort zone. Usually, I prefer micro-managing LLMs when writing code. This time, I was macro-managing.
I needed to create a mock history of the status of a manuscript, e.g. it was submitted on this date. THEN it moved to this state on this date. THEN … etc. I have no idea what the states could be, though. So, I could send it to an LLM, and it would give a different set of states each time. Or I could write a program and lose out on variety.
Instead, I asked ChatGPT.
Me: When looking up the status of a manuscript in a database, what are typical fields it might return?
ChatGPT: When looking up the status of a manuscript in a database, typical fields it might return include:
- Manuscript ID: A unique identifier for the manuscript.
- Title: The title of the manuscript.
- Author(s): Names of the author(s) who submitted the manuscript.
- Submission Date: The date the manuscript was submitted.
- Status: The current status of the manuscript (e.g., Under Review, Accepted, Rejected, In Revision).
- …
That helps. Then I said:
Me: Based on this, at any point, what “Status” values could a manuscript be in? Draw a mermaid flow graph for this.
ChatGPT: Here’s a mermaid flow graph depicting possible “Status” values a manuscript could be in throughout its lifecycle:
This is cool — it gives me a starting point that’s better than I could have conceived of.
Finally, I said:
Me: Write JS code that will randomly pick from one of these states and create records containing random dates and states representing the history of the manuscript up to that point
ChatGPT: Here’s a JavaScript code snippet that randomly picks from the manuscript states and creates records containing random dates and states, representing the history of the manuscript up to that point:
function getRandomDate(start, end) {
return new Date(start.getTime() + Math.random() * (end.getTime() - start.getTime()));
}
function getRandomElement(arr) {
return arr[Math.floor(Math.random() * arr.length)];
}
function generateManuscriptHistory() {
const history = [];
let currentState = "Submitted";
let currentDate = getRandomDate(new Date(2020, 0, 1), new Date());
history.push({ date: currentDate, state: currentState });
while (currentState !== "Accepted" && currentState !== "Rejected" && currentState !== "Published") {
let possibleNextStates;
switch (currentState) {
case "Submitted":
possibleNextStates = ["Under Review"];
break;
case "Under Review":
possibleNextStates = ["Review Completed"];
break;
case "Review Completed":
possibleNextStates = ["Accepted", "Rejected", "Major Revision Required", "Minor Revision Required"];
break;
case "Major Revision Required":
case "Minor Revision Required":
possibleNextStates = ["In Revision"];
break;
case "In Revision":
possibleNextStates = ["Resubmitted"];
break;
case "Resubmitted":
possibleNextStates = ["Under Review"];
break;
case "Accepted":
possibleNextStates = ["In Production"];
break;
case "In Production":
possibleNextStates = ["Published"];
break;
default:
possibleNextStates = [];
break;
}
if (possibleNextStates.length === 0) break;
currentState = getRandomElement(possibleNextStates);
currentDate = getRandomDate(currentDate, new Date());
history.push({ date: currentDate, state: currentState });
}
return history;
}
// Example usage:
console.log(generateManuscriptHistory());The above code implements a state-transition diagram — something I’ve never been good at doing. I have a subconscious bias, and I steer clear of even thinking about these. Now, I have one implemented (and it worked quite well!)
Yet another example that human imagination is the bottleneck to leveraging LLMs.
