In today’s episode, I reveal major time savings from AI. You’ll learn the “3x rule” for automation and how to build a task list. I explain approaches to eliminate repetitive work – from writing code to summarization. You’ll benefit from higher quality outputs while skipping manual effort. Join me for actionable tips to leverage models as virtual assistants across workflows.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode, Amy asks, What time savings are you finding with generative AI? A lot.
Here’s the rule of thumb.
And this is a trope from programming.
The the rule of thumb and programming is, if you do it more than three times, the machine should be doing it.
If you do a task more than three times, the machine should be doing it.
And prior to the advent of generative AI, that was sort of true.
There were things that traditional programming could do, that would help automate that would help get things done.
But it took a lot of time and very, very significant technical skill to make those pieces of software.
So really, developers were the ones who were able to use the power of coding to automate many of those monotonous tasks.
Today, language models can do that with either much less coding, or no code.
So for example, suppose you’ve got a spreadsheet.
And at the end of the month, you need to make a presentation out of the spreadsheet and that spreadsheet is just kind of a hot mess.
You can directly interpret that spreadsheet, say in a tool like chat GPT with the advanced data analysis module, and say, turn this ugly spreadsheet into this coherent table so that I can make a PowerPoint presentation with it.
You can do the same thing in Microsoft Copilot, Google Duet, and so on and so forth.
Or you could say to a tool like chat GPT, help me write some code in Python that extracts all this data, puts it into this format, and then makes PowerPoint slide and I can just open up the PPT x file that it creates.
Either one of those approaches is a time savings over doing it manually, which approach you take is going to depend on your comfort level with the with the product.
So as of today, you can’t get all the way to the finished product with just a little bit of time.
language model, because there are parts of that task that are not language, language based, right, like creating a PowerPoint slide is not a language based task.
It’s a it’s a mechanical task.
When it comes to writing code, language models can do that.
And then the code can perform those mechanical tasks, the code can take a table, reformat it, use map, matplotlib, or ggplot, depending on the language you’re working in, and generate the graphic and then create a PowerPoint slide contained for the graphic.
And spit that out and boom on your desktop, there’s your slide.
But that’s the rule of thumb.
Any task you do more than three times a machine should be doing it.
One of the things I talked about a lot is building out a task list.
What are the repetitive tasks that you do every day, every week, every month, every quarter, every year? Which of those tasks tasks are language based tasks? And could you get a machine to do them? Could you get a machine to take on that task of writing that summary email, or doing the executive summary on a set of slides? One thing that used to take me a ton of time in client reporting is I would go through a slide deck of reports.
And I would make manual annotations, like here’s what I see on this slide, here’s what this means.
And then in putting to the executive summary, I’d have to go through manually one slide by slide, try and remember or copy paste all the things I wrote throughout the slide deck and what a waste of time.
So I asked chat GPT one day, help me write a script that will export all the text within this, this PowerPoint.
And it took a couple iterations, but I got a Python script that does just that is dumps all the text, single text file.
And then I take that, put it into chat GPT and say out of all these notes that I made throughout this 70 slide deck, write an executive summary of 10 bullet points.
Christopher Penn: And it would go through and distill that down.
And now, instead of spending a couple hours with that very monotonous task, and I’m not really building any new information, right? I’m taking this, the notes that I already made.
It just gathers all that up summarizes it for me.
And boom, there’s my executive summary.
And the executive summary is better than I would have done.
It’s better than I would have done.
Because very often with that task, I would forget things or like, is that really important? Maybe, maybe not.
Christopher Penn: I would exercise flawed human judgment rather than taking all the text and trying to statistically summarize it down and just into a coherent cogent package.
The language models are better at that than I am.
And so not only am I using language models for a monotonous task, but the quality of my output has gone up because it’s sidestepping and circumventing my own human foibles my own flaws as a as a person as an analyst.
It takes all the information that I that I generated.
So it’s not like I’m having to do my work for me, I generated all the notes in the presentation already.
But now it’s distilling that down for me in a way that’s better than I would have done on my own.
So that’s how I would think about time savings with language models, find out all the different things that you do all the tasks that you do that are monotonous, that you do more than three times and say, Can I get a language model either to do this task for me, or help me build the tools I need to do this task? So really good question.
Thanks for asking.
We’ll talk to you soon.
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