[fingerthieff]

It's hard to really explain how the LLM decides what to recommend if I'm honest.

The general idea is I generate a prompt to feed into the LLM with specific instructions to use the Sonarr (for example) library info which is passed in as a list of series titles. I also provide some general guidelines on what it should take into account when choosing media to suggest.

After that it's in the hand of the LLM, it will internally analyze and recommend shows based on what it believe someone might enjoy based on their media library...Given that every LLM model is different, how they internally decide what shows to recommend will be unique to them. So if you have bad suggestions etc..It's best to try a different model.

it provides nice flexibility but in reality my control of the actual recommendations are limited to the initial prompt that is passed in.

[HyprMusic]

Does this mean it is limited to the model's internal memory? Meaning newer shows won't be in the recommendations because they're past the training cut-off?

[fingerthieff]

That is a likely true to an extent, though it's hard to say at what point it cuts off.

If a model was trained 6 months ago for example it will likely have some info on shows that came out this month due to various data points talking about that show as "upcoming" but not released. Due to that it may still recommend "new" shows that have just released.

All that being said, I have to imagine that suggesting shows that have just now been released is likely the weak point of the system for sure.