I've used it to proof emails for grammar, and it's done ok.
I'll also throw random programming questions into it, and it's been hit and miss. SO is probably still faster, and I like seeing the discussion. The problem with chatGPT right now is it gives an answer like it's certainty when it's often wrong.
I can see the benefits of this interaction model (basically summarizing all the things from a search into what feels like a person talking back), but I don't see change the world level hype at the moment.
I also wonder if LLMs will get worse over time through propagation error as content is generated by other LLMs.
I’m not the person you replied to but I’ve been using OpenAI’s API a lot for work. Some examples:
- Embedding free text data on safety observations, clustering them together, using text completion to automatically label the clusters, and identifying trends
- Embedding free text data on equipment failures. Some of our equipment failures have been classified manually by humans into various categories. I use the embeddings to train a model to predict those categories for uncategorized failures.
- Analyzing employee development goals and locating common themes. Then using this to identify where there are gaps we can fill in training offerings.
I've used it to proof emails for grammar, and it's done ok.
I'll also throw random programming questions into it, and it's been hit and miss. SO is probably still faster, and I like seeing the discussion. The problem with chatGPT right now is it gives an answer like it's certainty when it's often wrong.
I can see the benefits of this interaction model (basically summarizing all the things from a search into what feels like a person talking back), but I don't see change the world level hype at the moment.
I also wonder if LLMs will get worse over time through propagation error as content is generated by other LLMs.
I’m not the person you replied to but I’ve been using OpenAI’s API a lot for work. Some examples:
- Embedding free text data on safety observations, clustering them together, using text completion to automatically label the clusters, and identifying trends
- Embedding free text data on equipment failures. Some of our equipment failures have been classified manually by humans into various categories. I use the embeddings to train a model to predict those categories for uncategorized failures.
- Analyzing employee development goals and locating common themes. Then using this to identify where there are gaps we can fill in training offerings.