OpenAI released the largest study to date on how users really use ChatGPT. I have painstakingly synthesized the ones you and I should pay heed to, so you don’t have to wade through the plethora of useful and pointless insights.
TL;DR
- LLMs are not replacing search. But they are shifting how people access and consume information.
- Asking (49%) and Doing (40%) queries dominate the market and are increasing in quality.
- The top three use cases – Practical Guidance, Seeking Information, and Writing – account for 80% of all conversations.
- Publishers need to build linkable assets that add value. It can’t just be about chasing traffic from articles anymore.
Image Credit: Harry Clarkson-BennettChatbot 101
A chatbot is a statistical model trained to generate a text response given some text input. Monkey see, monkey do.
The more advanced chatbots have a two or more-stage training process. In stage one (less colloquially known as “pre-training”), LLMs are trained to predict the next word in a string.
Like the world’s best accountant, they are both predictable and boring. And that’s not necessarily a bad thing. I want my chefs fat, my pilots sober, and my money men so boring they’re next in line to lead the Green Party.
Stage two is where things get a little fancier. In the “post-training” phase, models are trained to generate “quality” responses to a prompt. They are fine-tuned on different strategies, like reinforcement learning, to help grade responses.
Over time, the LLMs, like Pavlov’s dog, are either rewarded or reprimanded based on the quality of their responses.
In phase one, the model “understands” (definitely in inverted commas) a latent representation of the world. In phase two, its knowledge is honed to generate the best quality response.
Without temperature settings, LLMs will generate exactly the same response time after time, as long as the training process is the same.
Higher temperatures (closer to 1.0) increase randomness and creativity. Lower temperatures (closer to 0) make the model(s) far more predictive and precise.
So, your use case determines the appropriate temperature settings. Coding should be set closer to zero. Creative, more content-focused tasks should be closer to one.
I have already talked about this in my article on how to build a brand post AI. But I highly recommend reading this very good guide on how temperature scales work with LLMs and how they impact the user base.
What Does The Data Tell Us?
That LLMs are not a direct replacement for search. Not even that close IMO. This Semrush study highlighted that LLM super users increased the amount of traditional searches they were doing. The expansion theory seems to hold true.
But they have brought on a fundamental shift in how people access and interact with information. Conversational interfaces have incredible value. Particularly in a workplace format.
Who knew we were so lazy?
1. Guidance, Seeking Information, And Writing…
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