LLMs don't really read documents like parsers do, from what I've observed, they behave like they first skim the document and find the section that they think are relevant to their task, they don't really try to read everything and hold it inside their context.
The solution to that is pretty simple: get to the point, list all the requirements that they must do complete for that task, constraints on what they must not do, and optional recommendations for them to follow. Leave things that they can figure out on their own out of these documents.
Not always. If an instruction is short and vague, then LLMs tend to fill in the blanks by guessing and the results are unpredictable. Semantic density is the key, prompts/instructions should be actionable and unambiguous over being terse and vague.
“It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience.”
The solution to that is pretty simple: get to the point, list all the requirements that they must do complete for that task, constraints on what they must not do, and optional recommendations for them to follow. Leave things that they can figure out on their own out of these documents.