Asking it to answer questions on its own is much more prone to hallucination.
To that end I've been using Llama 3 for summarizing transcripts of YouTube videos. It does a decent job, but... every single time (literally 100% of the time), it will hallucinate a random name for the speaker.* Every time! I thought it might be the system prompt, but there isn't one.
My own prompt is just "{text}\n\n###\n\nPlease summarize the text above."
If I ask it to summarize in bullet points, it doesn't do that.
I'm assuming there was something in the (instruct) training data that strongly encourages that, i.e. a format of summaries beginning with the author's name? Seems sensible enough, but obviously backfires when there's literally no data and it just makes something up...
*In videos where the speaker's name isn't in the transcript. If it's a popular field, it will often come up with something plausible (e.g. Andrew Ng for an AI talk.) If it's something more obscure, it'll dream up something completely random.
"{text}\n\n###\n\nPlease summarize the text above. The text is a video transcript. It may not have the names of the speakers in it. If you need to refer to an unnamed speaker, call them Speaker_1, Speaker_2 and so on."
"using the following documentation to guide you {api documentation}, edit this code {relevant code}, with the following objective: Replace uses of {old API calls} in {some function} with with relevant functions from the supplied documentation"
It mostly works, but if the context is a little to long, sometimes it will just spam the same umlaut or number (always umlaut's or numbers) over and over for example. Perhaps some fine-tuning of parameters like temp. or repetition penalty might fix it, time will tell.
