Translation (the problem transformers were invented to solve), Document QA, Embeddings for more semantic search, and unstructured data ingestion are all areas where LLMs excel.
When you have documentation to reference and keep in context, it’s pretty easy to achieve a low hallucination rate.
The hype is around like content creation and AGI, which is a nothingburger.
Still I don't understand the people that claim the shared LLMs can do that with internal documentation. That's bullshit and easily disproved, you can force it to work for toy problems, but only for toy problems.
But if you properly pre-process the documents and create a RAG type system (which uses embedding to find semantically similar docs before inserting them into LLM context) then it actually works quite well.
It’s good for big organizations with internal wikis, I’ve found.
It also works well for ingesting articles from online publications.
It did so by just reading the hugo documentation without any RAG.
And it was 10x better than the huge documentation in itself.
You won’t find that kind of ease of use with say webgpu+winit for building a small renderer in rust.
Right, which is why TFA specifically talks about "Generative AI".