cognitive-scaffolding

AI agents ignore critical instructions in long context windows due to inefficient placement of evidence and constraints. Restructuring prompts using zone-based assembly with anchor, foreground, and chunked middle sections prevents this performance drop-off.

Structure prompts for multi-document reasoning by applying cognitive scaffolding: place immutable rules at the start, the current task at the end, and chunk large reference data into explicitly delimited middle sections to maintain attention.

Prevent lost in the middle behavior in transformer prompting by using attention-aware assembly with clear delimiters. This organizes information into anchor, structured middle, background, and foreground zones to match attention patterns.

Yes, you can use cognitive scaffolding for code review tasks. It organizes large reference data into explicitly delimited background and middle sections, ensuring the model retrieves both task-critical constraints and fallback information efficiently.

Use attention allocation techniques for prompt engineering when your model intermittently ignores constraints during multi-document reasoning or code review, requiring zone-based context placement to emphasize immutable rules and current user intent.