Migrating from GPT-3.5-turbo to GPT-4o-mini
Guess when I switched models.
Recently OpenAI released GPT-4o-mini — a new flagship model for the cheap segment, as it were.
- They say it works "almost like" GPT-4o, sometimes even better than GPT-4.
- It is almost three times cheaper than GPT-3.5-turbo.
- Context size 128k tokens, against 16k for GPT-3.5-turbo.
Of course, I immediately started migrating my news reader to this model.
In short, it's a cool replacement for GPT-3.5-turbo. I immediately replaced two LLM agents with one without changing prompts, reducing costs by a factor of 5 without losing quality.
However, then I started tuning the prompt to make it even cooler and began to encounter nuances. Let me tell you about them.
My GPTs and prompt engineering
Ponies are doing prompt engineering (c) DALL-E
I've been using ChatGPT almost since the release of the fourth version (so for over a year now). Over this time, I've gotten pretty good at writing queries to this thing.
At some point, OpenAI allowed customizing chats with your text instructions (look for Customize ChatGPT in the menu). With time, I added more and more commands there, and recently, the size of the instructions exceeded the allowed maximum :-)
Also, it turned out that a universal instruction set is not such a good idea — you need to adjust instructions for different kinds of tasks, otherwise, they won't be as useful as they could be.
Therefore, I moved the instructions to GPT bots instead of customizing my chat. OpenAI calls them GPTs. They are the same chats but with a higher limit on the size of the customized instructions and the ability to upload additional texts as a knowledge base.
Someday, I'll make a GPT for this blog, but for now, I'll tell you about two GPTs I use daily:
- Expert — answers to questions.
- Abstractor — makes abstracts of texts.
For each, I'll provide the basic prompt with my comments.
By the way, OpenAI recently opened a GPT store, I'd be grateful if you liked mine GPTs. Of course, only if they are useful to you.
Two years of writing RFCs — statistics
Slightly more than two years ago, I became a Lead/Engineering Manager for Palta's payment team. I left the company at the end of 2023 for another sabbatical [ru].
It is time to sum up. I will start with my favorite initiative.
From the first month, I promoted the idea of preceding major changes with text documents — RFC — Request for Comments.
In this post, I will analyze two years of applying this practice to share the experience, summarize the results, and have convincing arguments for my next job.
Preferences of strategy players
Looking at the survey data and trying to find something useful.
Recently I've conducted a survey about the preferences of strategy players.
In the previous post, we cleaned up the data, and in this one, we will try to find insights within it.
In this post you will find an interactive dashboard with a bunch of charts, where you can compare two samples of your choice. There are many samples — for every taste and color, so feel free to explore and share the patterns you find on Telegram and Discord.
But be careful with conclusions. There is little data, in some cases very little. For example, the difference between the sample sizes of male and female respondents is about tenfold => you should be very careful in interpreting the differences between them.
In general, do not take this post as a full-fledged study. I'm sure many analysts would have torn my hands off for such a thing. Then sewed them back and torn them off again :-D Use the post as an interface to the data, and make your own conclusions.
Cleaning up the results of the strategy players survey
The survey was targeted at the fans of strategy games. It seems, I was able to find audience quite accurately.
Recently I asked you to fill in a survey about strategy games.
Thank you to everyone who took the time to do this. It's time to share the results.
363 respondents filled in the survey. 304 answers remained after data normalization and cleaning.
There will be two posts:
- This one — about the methodology of data collection and processing, and their cleaning. Cleaned data will be shared.
- The next one — about the analysis of the results.