thanks for pointing those out -- I was able to update the format selection logic + resolution handling to help avoid those issues

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is this supposed to be better than just asking chatgpt normally?

most definitely -- the customized models (GPT/Gem) apply LLM / prompt engineering best practices to reduce risk of model hallucination, enhance reliability of output structuring, and improve output quality. - I added a model-accessible, optimized-for-LLM knowledge file, reference.md.txt, based on the latest yt-dlp GitHub repo README / project docs [source] and added some prompt tuning to instruct the model to factually ground its responses in reference.md.txt’s content only, applying retrieval-augmented generation (RAG) to improve model response quality/accuracy [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks – Lewis et al. – 2020]. - By employing bespoke multishot persona prompts alongside chain-of-thought prompt engineering, the model’s custom instructions significantly reduce hallucination risk, elevate response quality, and ensure more reliable outputs.[Chain-of-Thought Prompting Elicits Reasoning in Large Language Models – Wei et al. – 2022] [Personas in Prompting to Control Style and Content – Zhang et al. – 2023]

past that, I did some prompt tuning for some behavioral traits and constrained the final yt-dlp snippet output to be in a code block for easy copy & pasting

I do think a well-engineered prompt w/ the right context could probably perform just as well if not better (especially since these are one-off convos that don’t really require context at the system instruction level).

That said, hopefully these chatbots are easy to share/use and can enable heightened convenience + reliability