As mentioned above, I don't think you'd need to train your own model for this (or for most use cases of this, anyway). You'd use a RAG. I've tried out working

As mentioned above, I don't think you'd need to train your own model for this (or for most use cases of this, anyway). You'd use a RAG. I've tried out working with custom documents in two different ways for different types of data: * Once using LlamaIndex + Chroma[0] to transcribe and then conversationally query video contents (using GPT 3.5 or 4 as the backing LLM). * Once using GPT Plus, uploading long-form PDFs of my own fiction books to the GPT's knowledge base. I use this to help me remember character names and timelines (not always accurate, so results need to be treated with caution) and help brainstorm story or tech ideas for my world. Both work for what I'm using them for. I feel like option one is more customizable and easier to tweak for the types of results I would want, if I have concrete requirements about what kind of output I'm looking for. Option two has a lower barrier to entry and is just a little lower effort (no need to run your own app). For the next iteration, I'd like to try out AWS Bedrock and compare the workflow and results. [0] https://www.daily.co/blog/search-your-video-content-library-with-llamaindex-and-chroma/ https://www.daily.co/blog/search-your-video-content-library-...