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.

## 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- LLMs have **parametric knowledge** (encoded during training) = static, outdated, non-attributable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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### Context Stuffing vs. Context Engineering

| Dimension | Context Stuffing | Context Engineering |
|-----------|------------------|---------------------|
| **Mindset** | Volume = quality | Structure = quality |
| **Approach** | "Add everything just in case" | "What decision am I making?" |
| **Persistence** | Persist all context | Retrieve with intent |
| **Agent Chains** | Share everything between agents | Bounded context per agent |
| **Failure Response** | Retry until it works | Fix the structure |
| **Economic Model** | Context as storage | Context as attention (scarce resource) |

**Critical Metaphor:** Context stuffing is like bringing your entire file cabinet to a meeting. Context engineering is bringing only the 3 documents relevant to today's decision.

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### The Anti-Pattern: Context Stuffing

**Five Markers of Context Stuffing:**
1. **Reflexively expanding context windows** — "Just add more tokens!"
2. **Persisting everything "just in case"** — No clear retention criteria
3. **Chaining agents without boundaries** — Agent A passes everything to Agent B to Agent C
4. **Adding evaluations to mask inconsistency** — "We'll just retry until it's right"
5. **Normalized retries** — "It works if you run it 3 times" becomes acceptable

**Why It Fails:**
- **Reasoning Noise:** Thousands of irrelevant files compete for attention, degrading multi-hop logic
- **Context Rot:** Dead ends, past errors, irrelevant data accumulate → goal drift
- **Lost in the Middle:** Models prioritize beginning (primacy) and end (recency), ignore middle
- **Economic Waste:** Every query becomes expensive without accuracy gains
- **Quantitative Degradation:** Accuracy drops below