The conclusion you walked away with is the opposite of what usually works in practice.
The more context you give the llm, the better.
The key takeaway from that paper is to keep your instructions/questions/direction in the beginning or at the end of the context. Any information can go anywhere.
Not to be too dismissive, it's a good paper, but we're one year further and in practice this issue seems to have been tackled by training on better data.
This can differ a lot depending on what model you're using, but in the case of claude sonnet 3.5, more relevant context is generally better for anything except for speed.
It does remain true that you need to keep your most important instructions at the beginning or at the end however.
The more context you give the llm, the better.
The key takeaway from that paper is to keep your instructions/questions/direction in the beginning or at the end of the context. Any information can go anywhere.
Not to be too dismissive, it's a good paper, but we're one year further and in practice this issue seems to have been tackled by training on better data.
This can differ a lot depending on what model you're using, but in the case of claude sonnet 3.5, more relevant context is generally better for anything except for speed.
It does remain true that you need to keep your most important instructions at the beginning or at the end however.