> If I had to invest in any one area of LLM usage, it would be this. There is so much unstructured data in the world, and converting things like legal contracts or chatlogs into structured, queryable data is absurdly powerful.

I tested poorly OCR'd text from a late 1800s magazine and was pretty impressed with the results from both GPT-4 and even Bard. In addition to making inferences based on the letters, it was able to infer from context the historically accurate terms. However, one prompt asking it to correct words didn't work as well as two prompts with the first one listing candidates for bad OCR'd words.

While that might not seem like what you're talking about, it has the benefit of adding to the overall corpus that the future models are trained on. Also, with the NYT lawsuit, I'd presume fixing OCRs of old magazines and articles would be a pretty good way to fill the gap left behind.