A team has written a complex 2,000-token system prompt with nested instructions but observes that Claude frequently follows earlier instructions while ignoring those in the middle of the prompt. Which structural technique most directly addresses this attention degradation issue?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — large language models including Claude exhibit recency and primacy effects — they tend to weight instructions at the beginning and end of context more heavily than those in the middle. The mitigation is to: (1) place critical instructions at both the beginning and end of the system prompt to capture both effects, (2) use XML tags to create explicit hierarchical sections that Claude's training associates with structured importance, and (3) explicitly state instruction priority ('If any rules conflict, priority order is: 1.
Full explanation below image
Full Explanation
Large language models including Claude exhibit recency and primacy effects — they tend to weight instructions at the beginning and end of context more heavily than those in the middle. The mitigation is to: (1) place critical instructions at both the beginning and end of the system prompt to capture both effects, (2) use XML tags to create explicit hierarchical sections that Claude's training associates with structured importance, and (3) explicitly state instruction priority ('If any rules conflict, priority order is: 1. Safety 2. Format 3. Style'). Option A (more max_tokens) is irrelevant — this is an input attention issue, not an output length issue. Option C (markdown headers) helps readability but doesn't specifically address middle-of-prompt attention degradation. Option B fragments instructions across calls, losing holistic coherence needed for consistent behavior.