Note that I have no knowledge whatsoever of how ESPN work, I'm inferring from what I've seen elsewhere.

On the other hand, the rather simple task of "here's a set of goals, their times, who made them, who assisted... turn that into prose" could even be done without LLMs with a deterministic algorithm, and may very well have been in this case. Some of the grammar issues in the OP feel very pre-LLM in nature, like a combination of substitution rules gone awry.

Now, could you create a system that repeatedly interrogates the statements made by a first pass of an LLM on summarizing a long transcript, and comparing those results against structured data you know for accuracy? Would this lead to richer content and accessible error rates relative to the simpler approach? Would this be the type of thing that the best machine learning engineers in the world could probably prototype over a hackathon? The answer is very possibly yes to all three of these. But it's far from low-hanging fruit for any sizable, risk-averse organization. It's very difficult to fight against "the thing we have is imperfect, but at least it never gets the facts wrong."

Here is a transcript of a soccer match. In the style of an experienced professional sports reporter, please write a 200 word article about the match.

Its summary starts, "In her final professional match, Alex Morgan delivered a performance filled with emotion and resilience, though her San Diego Wave fell short in a 3-1 loss to North Carolina Courage. The game at Snapdragon Stadium in San Diego was more than just a contest; it was a tribute to one of soccer’s most iconic figures."

It did get the score wrong. Here's the rest:

1. https://chatgpt.com/share/de8c60d1-69ab-4291-99dc-d4d95af3d3...

One thing to try is only using the post-match commentary, which starts after the line containing 'final whistle.' When I did this, the result was more factually accurate, while still focusing on Morgan.

https://chatgpt.com/share/9c122702-b46f-4e5e-bd05-e063a74126...