When I search this topic online, I always find either wrong information or advertising lies. So what is actually something that LLMs can do very well, as in being actually useful and not just outputing a nonsensical word salad that sounds coherent.
Results
So basically from what I’ve read, most people use it for natural language processing problems.
Example: turn this infodump into a bullet point list, or turn this bullet point list into a coherent text, help me with rephrasing this text, word association, etc.
Other people use it for simple questions that it can answer with a database of verified sources.
Also, a few people use it as struggle duck, basically helping alleviate writers block.
Thanks guys.
I think using LLMs with RAG (aka tools) is more useful and reliable than relying only on training data that the model does its best to represent.
For example, using a search engine to find results for a query, downloading the first 10 results as text, and then having the LLM answer subsequent queries about those sources, or another example would be uploading a document and having the LLM answer queries about its contents.
This is also advantageous because much smaller and quicker models can be used while still producing accurate results (often with citations to the source).
This can even be self hosted with Open WebUI/ollama.