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.
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.
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.