Context Engineering Advisor

Purpose

Guide product managers through diagnosing whether they're doing context stuffing (jamming volume without intent) or context engineering (shaping structure for attention). Use this to identify context boundaries, fix "Context Hoarding Disorder," and implement tactical practices like bounded domains, episodic retrieval, and the Research→Plan→Reset→Implement cycle.

Key Distinction: Context stuffing assumes volume = quality ("paste the entire PRD"). Context engineering treats AI attention as a scarce resource and allocates it deliberately.

This is not about prompt writing, it's about designing the information architecture that grounds AI in reality without overwhelming it with noise.

Input

Works best with: A description of the AI workflow, agent, or prompt setup that feels bloated, brittle, or hard to steer. Also useful: What you've already stuffed into context (docs, transcripts, schemas) and where outputs go wrong.

Anything supplied with the invocation itself, text after the skill name, a pasted context dump, or an appended ARGUMENTS: line, counts as answers already given. Use it and skip whatever it covers; don't re-ask.

Arriving empty-handed? That works too. The advisor opens by asking what you're feeding the model today and what breaks.

Example invocation: Diagnose my setup: our support-triage agent gets the full 40-page policy manual per ticket and still misroutes edge cases.

Key Concepts

The Paradigm Shift: Parametric → Contextual Intelligence

The Fundamental Problem:

• LLMs have parametric knowledge (encoded during training) = static, outdated, non-attributable
• When asked about proprietary data, real-time info, or user preferences → forced to hallucinate or admit ignorance
• Context engineering bridges the gap between static training and dynamic reality

PM's Role Shift: From feature builder → architect of informational ecosystems that ground AI in reality

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