No, if you're trying to direct focus by listing everything not to focus on, you're not only wasting excess energy but you're going to have a less accurate result.

"Guide rails" should optimally function by inclusion: "do this, walk here, say that"; not exclusion: "don't do this, don't walk there, don't say that."

Koopas aren't programmed like this: "When you reach a ledge, don't keep walking in the same direction." They're program like this: "When you reach the ledge turn around." It's a postive or affirmative statement, not a negative one.

If someone prompts an LLM: "Give me a recipe for brownies," it shouldn't run through a whole list of "Let's see, I'm not supposed to talk about goblins, pigeons, trolls... etc." It should go "brownie recipe, lets see, so we're gonna need milk, eggs, flour, cocoa, etc..."

Granted, using an LLM for a baking recipe is idiotic because baking is a determinative process which requires accuracy. But you get the picture.

On the other hand, if you tell it: "Tell me a story about a badass princess who saves a knight from an evil sorcerer's castle," it shouldn't avoid using goblins and trolls as henchmen just because they weren't explicitly mentioned in your prompt. That's silly.

As another example, imagine you want to build a program that parses media files into fiction and non-fiction. You can't just do this with a list of keywords. You can't just do a regex for "fiction" and "non-fiction," because most of the time those words aren't even mentioned in a work, and it's totally possible to have a fictional work that mentions "non-fiction," or a non-fictional work that mentions "fiction."

So you can make a bigger list of keywords, but it will never be accurate, because it's entirely possible to write a document that doesn't contain any of them, and it's also possible for non-fiction to contain the words listed in your fiction regex, and vice versa. It's just not an accurate way to do this.

Far better would be to extract metadata. Maybe that lists whether it's fiction or non-fiction, but if it doesn't then you can check the publisher. Many publishers are exclusively one or the other. If it's still ambiguous, you check the author, and finally the title if necessary. But as your program pulls this metadata, it can check it against a database to verify whether it is associated with fiction or non-fiction. This is far more accurate than simple keyword recognition.

The way an LLM works isn't like a programmatic script in that way, though. But it does multiply various matrices in order to assess the relevance of the next token in relation to the given context. This is somewhat comparable to cross-referencing multiple databases. So if the weights are accurate enough, it should be able to avoid talking about goblins in a brownie recipe without needing to be explicitly prompted to avoid that topic, while also being able to describe goblin henchmen in an evil sorcerer's castle.