context-engineering-advisor

Context stuffing occurs when AI assistants receive excessive irrelevant data, degrading reliability. It happens when teams fail to separate decision-driven context from noise, causing memory architectures and retrieval systems to fail.

Design a two-layer memory architecture by creating a Context Manifest to formalize what belongs in memory versus what requires retrieval. This separates persistent context from episodic data, keeping execution clean.

The Research Plan Reset Implement cycle prevents context rot by resetting the AI context window after research and planning phases, ensuring clean execution during implementation without prior noise interference.

Define context boundaries by establishing clear ownership and a Context Manifest that formalizes what belongs in bounded contexts. This separates needed context from noise and dictates episodic retrieval triggers.

Your product management workflow needs a Context Manifest if AI assistants suffer from context stuffing. It formalizes memory ownership, separating what belongs in context from what should be retrieved on demand.

Use bounded contexts to define stable memory boundaries for persistent AI knowledge, while episodic retrieval handles specific situational data. A Context Manifest distinguishes between both to prevent context rot.