deanpeters/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• LLMs have parametric knowledge (encoded during training) = static, outdated, non-attributable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

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.

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 20% when context exceeds ~32k tokens

The Hidden Costs:

• Escalating token consumption
• Diluted attention across irrelevant material
• Reduced output confidence
• Cascading retries that waste time and money

Real Context Engineering: Core Principles

Five Foundational Principles:

1. Context without shape becomes noise
2. Structure > Volume
3. Retrieve with intent, not completeness
4. Small working contexts (like short-term memory)
5. Context Compaction: Maximize density of relevant information per token

Quantitative Framework:

Efficiency = (Accuracy × Coherence) / (Tokens × Latency)

Key Finding: Using RAG with 25% of available tokens preserves 95% accuracy while significantly reducing latency and cost.

The 5 Diagnostic Questions (Detect Context Hoarding Disorder)

Ask these to identify context stuffing:

1. What specific decision does this support? — If you can't answer, you don't need it
2. Can retrieval replace persistence? — Just-in-time beats always-available
3. Who owns the context boundary? — If no one, it'll grow forever
4. What fails if we exclude this? — If nothing breaks, delete it
5. Are we fixing structure or avoiding it? — Stuffing context often masks bad information architecture

Memory Architecture: Two-Layer System

Short-Term (Conversational) Memory:

• Immediate interaction history for follow-up questions
• Challenge: Space management → older parts summarized or truncated
• Lifespan: Single session

Long-Term (Persistent) Memory:

• User preferences, key facts across sessions → deep personalization
• Implemented via vector database (semantic retrieval)
• Two types:
• Declarative Memory: Facts ("I'm vegan")
• Procedural Memory: Behavioral patterns ("I debug by checking logs first")
• Lifespan: Persistent across sessions

LLM-Powered ETL: Models generate their own memories by identifying signals, consolidating with existing data, updating database automatically.

The Research → Plan → Reset → Implement Cycle

The Context Rot Solution:

1. Research: Agent gathers data → large, chaotic context window (noise + dead ends)
2. Plan: Agent synthesizes into high-density SPEC.md or PLAN.md (Source of Truth)
3. Reset: Clear entire context window (prevents context rot)
4. Implement: Fresh session using only the high-density plan as context

Why This Works: Context rot is eliminated; agent starts clean with compressed, high-signal context.

Anti-Patterns (What This Is NOT)

• Not about choosing AI tools — Claude vs. ChatGPT doesn't matter; architecture matters
• Not about writing better prompts — This is systems design, not copywriting
• Not about adding more tokens — "Infinite context" narratives are marketing, not engineering reality
• Not about replacing human judgment — Context engineering amplifies judgment, doesn't eliminate it

When to Use This Skill

✅ Use this when:

• You're pasting entire PRDs/codebases into AI and getting vague responses
• AI outputs are inconsistent ("works sometimes, not others")
• You're burning tokens without seeing accuracy improvements
• You suspect you're "context stuffing" but don't know how to fix it
• You need to design context architecture for an AI product feature

❌ Don't use this when:

• You're just getting started with AI (start with basic prompts first)
• You're looking for tool recommendations (this is about architecture, not tooling)
• Your AI usage is working well (if it ain't broke, don't fix it)

Facilitation Source of Truth

Use workshop-facilitation as the default interaction protocol for this skill.

It defines:

• session heads-up + entry mode (Guided, Context dump, Best guess)
• one-question turns with plain-language prompts
• progress labels (for example, Context Qx/8 and Scoring Qx/5)
• interruption handling and pause/resume behavior
• numbered recommendations at decision points
• quick-select numbered response options for regular questions (include Other (specify) when useful)

This file defines the domain-specific assessment content. If there is a conflict, follow this file's domain logic.

Application

This interactive skill uses adaptive questioning to diagnose context stuffing, identify boundaries, and provide tactical implementation guidance.

Step 0: Gather Context

Agent asks:

Before we diagnose your context practices, let's gather information:

Current AI Usage:

• What AI tools/systems do you use? (ChatGPT, Claude, custom agents, etc.)
• What PM tasks do you use AI for? (PRD writing, user research synthesis, discovery, etc.)
• How do you provide context? (paste docs, reference files, use projects/memory)

Symptoms:

• Are AI outputs inconsistent? (works sometimes, not others)
• Are you retrying prompts multiple times to get good results?
• Are responses vague or hedged despite providing "all the context"?
• Are token costs escalating without accuracy improvements?

System Architecture (if applicable):

• Do you have custom AI agents or workflows?
• How is context shared between agents?
• Do you use RAG, vector databases, or memory systems?

You can describe briefly or paste examples.

Step 1: Diagnose Context Stuffing Symptoms

Agent asks:

Let's assess whether you're experiencing context stuffing. Which of these symptoms do you recognize?

Select all that apply:

1. "I paste entire documents into AI" — Full PRDs, complete user interview transcripts, entire codebases
2. "AI gives vague, hedged responses despite having 'all the context'" — Responses like "it depends," "consider these options," non-committal
3. "I have to retry prompts 3+ times to get usable output" — Inconsistency is normalized
4. "Token costs are escalating but accuracy isn't improving" — Spending more, getting same or worse results
5. "I keep adding more context hoping it'll help" — Reflexive expansion without strategy
6. "My agents pass everything to each other" — Agent A → Agent B → Agent C with full context chain
7. "I don't have clear criteria for what to include/exclude" — No context boundary definitions
8. "None of these—my AI usage is working well" — Skip to advanced optimization

User response: Select symptoms]

Agent analyzes:

Based on your selections:

• 0-1 symptoms: Healthy context practices; proceed to optimization
• 2-3 symptoms: Early context stuffing; address before it scales
• 4+ symptoms: Active Context Hoarding Disorder; immediate intervention needed

Agent proceeds to diagnostic questions.

Step 2: Diagnostic Question 1 — What Specific Decision Does This Support?

Agent asks:

Let's start with the most critical question: For each piece of context you're including, what specific decision does it support?

Example Context Analysis:

Imagine you're asking AI to "help with discovery planning." You're providing:

• Entire PRD (20 pages)
• 50 user interview transcripts (full)
• Competitive analysis doc (15 pages)
• Team meeting notes from last 3 months

Question: What decision are you making right now?

Offer 3 scenarios:

1. "I'm deciding which user segment to interview first"

• Context needed: User segments from PRD (2 paragraphs), prior interview themes (1 page synthesis), not full transcripts
• Context NOT needed: Meeting notes, full competitive analysis, full PRD

1. "I'm deciding which discovery questions to ask in interviews"

• Context needed: Research objectives (from PRD), past interview insights (synthesis), Jobs-to-be-Done framework
• Context NOT needed: Full competitive analysis, full meeting notes

1. "I'm not sure what decision I'm making—I just want AI to 'understand my product'"

• Problem: No specific decision = context stuffing trap
• Fix: Define the decision first, then select context

Agent recommends:

Best Practice: Before adding context, complete this sentence: > "I need this context because I'm deciding specific decision], and without specific information], I can't make that decision."

If you can't complete that sentence, you don't need the context.

User response: Describe their decision + context]

Agent validates: Does the context directly support the stated decision? If not, recommend trimming.

Step 3: Diagnostic Question 2 — Can Retrieval Replace Persistence?

Agent asks:

Second question: Is this information you always need, or something you can retrieve just-in-time?

The Distinction:

Always-Needed (Persist):

• Core product constraints (technical, regulatory, strategic)
• User preferences that apply to every interaction
• Critical definitions (operational glossary)
• Non-negotiable rules

Episodic (Retrieve on-demand):

• Project-specific details (this epic, this sprint)
• Historical data (past PRDs, old interview transcripts)
• Contextual facts (competitive analysis, market research)
• Temporary decisions

Key Insight: Just-in-time retrieval beats always-available. Don't persist what you can retrieve.

Offer 3 options:

1. "Most of my context is always-needed (core constraints, user prefs)"

• Assessment: Good instinct; verify with Question 4 (what fails if excluded?)
• Recommendation: Build constraints registry and operational glossary (persist these)

1. "Most of my context is episodic (project details, historical data)"

• Assessment: Perfect candidate for RAG or retrieval
• Recommendation: Implement semantic search; retrieve only relevant chunks for each query

1. "I'm not sure which is which—I persist everything to be safe"

• Assessment: Classic Context Hoarding Disorder symptom
• Fix: Apply Question 4 test to each piece of context

Agent recommends:

Rule of Thumb:

• Persist: Information referenced in 80%+ of interactions
• Retrieve: Information referenced in <20% of interactions
• Gray zone (20-80%): Depends on retrieval latency vs. context window cost

User response: Categorize their context]

Agent provides: Specific recommendations on what to persist vs. retrieve.

Step 4: Diagnostic Question 3 — Who Owns the Context Boundary?

Agent asks:

Third question: Who is responsible for defining what belongs in vs. out of your AI's context?

The Ownership Problem:

If no one owns the context boundary, it will grow indefinitely. Every PM will add "just one more thing," and six months later, you're stuffing 100k tokens per query.

Offer 3 options:

1. "I own the boundary (solo PM or small team)"

• Assessment: Good—you can make fast decisions
• Recommendation: Document your boundary criteria (use Questions 1-5 as framework)

1. "My team shares ownership (collaborative boundary definition)"

• Assessment: Can work if formalized
• Recommendation: Create a "Context Manifest" doc: what's always included, what's retrieved, what's excluded (and why)

1. "No one owns it—it's ad-hoc / implicit"

• Assessment: Critical risk; boundary will expand uncontrollably
• Fix: Assign explicit ownership; schedule quarterly context audits

Agent recommends:

Best Practice: Create a Context Manifest

markdown
# Context Manifest: [Product/Feature Name]

## Always Persisted (Core Context)
- Product constraints (technical, regulatory)
- User preferences (role, permissions, preferences)
- Operational glossary (20 key terms)

## Retrieved On-Demand (Episodic Context)
- Historical PRDs (retrieve via semantic search)
- User interview transcripts (retrieve relevant quotes)
- Competitive analysis (retrieve when explicitly needed)

## Excluded (Out of Scope)
- Meeting notes older than 30 days (no longer relevant)
- Full codebase (use code search instead)
- Marketing materials (not decision-relevant)

## Boundary Owner: [Name]
## Last Reviewed: [Date]
## Next Review: [Date + 90 days]

User response: Describe current ownership model]

Agent provides: Recommendation on formalizing ownership + template for Context Manifest.

Step 5: Diagnostic Question 4 — What Fails if We Exclude This?

Agent asks:

Fourth question: For each piece of context, what specific failure mode occurs if you exclude it?

This is the falsification test. If you can't identify a concrete failure, you don't need the context.

Offer 3 scenarios:

1. "If I exclude product constraints, AI will recommend infeasible solutions"

• Failure Mode: Clear and concrete
• Assessment: Valid reason to persist constraints

1. "If I exclude historical PRDs, AI won't understand our product evolution"

• Failure Mode: Vague and hypothetical
• Assessment: Historical context rarely needed for current decisions
• Fix: Retrieve PRDs only when explicitly referencing past decisions

1. "If I exclude this, I'm not sure anything would break—I just include it to be thorough"

• Failure Mode: None identified
• Assessment: Context stuffing; delete immediately

Agent recommends:

The Falsification Protocol:

For each context element, complete this statement: > "If I exclude context element], then specific failure] will occur in specific scenario]."

Examples:

• ✅ Good: "If I exclude GDPR constraints, AI will recommend features that violate EU privacy law."
• ❌ Bad: "If I exclude this PRD, AI might not fully understand the product." (Vague)

User response: Apply falsification test to their context]

Agent provides: List of context elements to delete (no concrete failure identified).

Step 6: Diagnostic Question 5 — Are We Fixing Structure or Avoiding It?

Agent asks:

Fifth question: Is adding more context solving a problem, or masking a deeper structural issue?

The Root Cause Question:

Context stuffing often hides bad information architecture. Instead of fixing messy, ambiguous documents, teams add more documents hoping AI will "figure it out."

Offer 3 options:

1. "I'm adding context because our docs are poorly structured/ambiguous"

• Assessment: You're masking a structural problem
• Fix: Clean up the docs first (remove ambiguity, add constraints, define terms)
• Example: Instead of pasting 5 conflicting PRDs, reconcile them into 1 Source of Truth

1. "I'm adding context because we don't have a shared operational glossary"

• Assessment: You're compensating for missing foundations
• Fix: Build the glossary (20-30 key terms); AI can reference it reliably
• Example: Define "active user," "churn," "engagement" unambiguously

1. "I'm adding context because our constraints aren't documented"

• Assessment: You're avoiding constraint engineering
• Fix: Create constraints registry (technical, regulatory, strategic)
• Example: Document "We won't build mobile apps" vs. explaining it in every prompt

Agent recommends:

The Structural Health Test:

If you're adding context to compensate for:

• Ambiguous documentation → Fix the docs, don't add more
• Undefined terms → Build operational glossary
• Undocumented constraints → Create constraints registry
• Conflicting information → Reconcile into Source of Truth

User response: Identify structural issues]

Agent provides: Prioritized list of structural fixes before adding more context.

Step 7: Define Memory Architecture

Agent asks:

Based on your context needs, let's design a two-layer memory architecture.

Your Context Profile (from previous steps):

• Always-needed context: Summary from Q2]
• Episodic context: Summary from Q2]
• Boundary owner: From Q3]
• Validated essentials: From Q4]
• Structural fixes needed: From Q5]

Recommended Architecture:

Short-Term (Conversational) Memory:

• What it stores: Immediate interaction history for follow-up questions
• Lifespan: Single session
• Management: Summarize or truncate older parts to avoid crowding
• Your specific needs: Agent customizes based on user's workflow]

Long-Term (Persistent) Memory:

• What it stores: User preferences, core constraints, operational glossary
• Lifespan: Persistent across sessions
• Implementation: Vector database (semantic retrieval)
• Two types:
• Declarative Memory: Facts (e.g., "We follow HIPAA regulations")
• Procedural Memory: Behavioral patterns (e.g., "Always validate feasibility before usability")
• Your specific needs: Agent customizes]

Retrieval Strategy (Episodic Context):

• What it retrieves: Historical PRDs, user interviews, competitive analysis
• Method: Semantic search triggered by query intent
• Optimization: Contextual Retrieval (Anthropic) — prepend explanatory context to each chunk before embedding
• Your specific needs: Agent customizes]

Agent offers:

Would you like me to:

1. Generate a Context Architecture Blueprint for your specific use case?
2. Provide implementation guidance (tools, techniques, best practices)?
3. Design a retrieval strategy for your episodic context?

User response: Selection]

Step 8: Implement Research → Plan → Reset → Implement Cycle

Agent asks:

Now let's implement the Research → Plan → Reset → Implement cycle to prevent context rot.

The Problem: As agents research, context windows grow chaotic—filled with dead ends, errors, and noise. This dilutes attention and causes goal drift.

The Solution: Compress research into a high-density plan, then clear the context window before implementing.

The Four-Phase Cycle:

Phase 1: Research (Chaotic Context Allowed)

• Agent gathers data from multiple sources
• Context window grows large and messy (this is expected)
• Dead ends, failed hypotheses, and noise accumulate
• Goal: Comprehensive information gathering

Phase 2: Plan (Synthesis)

• Agent synthesizes research into a high-density SPEC.md or PLAN.md
• This becomes the Source of Truth for implementation
• Key elements:
• Decision made
• Evidence supporting decision
• Constraints applied
• Next steps (sequenced)
• Format: Structured, concise, unambiguous

Phase 3: Reset (Clear Context Window)

• Critical step: Clear the entire context window
• Delete all research artifacts, dead ends, errors
• This prevents context rot from poisoning implementation

Phase 4: Implement (Fresh Session with Plan Only)

• Start a new session with only the high-density plan as context
• Agent has clean, focused attention on execution
• No noise from research phase

Agent offers 3 options:

1. "I want a template for the PLAN.md format"

• Agent provides structured template for high-density plans

1. "I want to see an example of this cycle in action"

• Agent walks through concrete PM use case (e.g., discovery planning)

1. "I'm ready to implement this in my workflow"

• Agent provides step-by-step implementation guide

User response: Selection]

Agent provides: Tailored guidance based on selection.

Step 9: Action Plan & Next Steps

Agent synthesizes:

Based on your context engineering assessment, here's your action plan:

Immediate Fixes (This Week):

1. Delete context with no falsifiable failure mode from Q4]
2. Apply Research→Plan→Reset→Implement to your next AI task]
3. Document context boundary in Context Manifest]

Foundation Building (Next 2 Weeks):

1. Build constraints registry with 20+ entries]
2. Create operational glossary with 20-30 key terms]
3. Implement two-layer memory architecture]

Long-Term Optimization (Next Month):

1. Set up semantic retrieval for episodic context]
2. Assign context boundary owner + quarterly audit schedule]
3. Implement Contextual Retrieval (Anthropic) for RAG]

Success Metrics:

• Token usage down 50%+ (less context stuffing)
• Output consistency up (less retry/regeneration)
• Response quality up (sharper, less hedged answers)
• Context window stable (no unbounded growth)

Agent offers:

Would you like me to:

1. Generate specific implementation docs (Context Manifest, PLAN.md template, etc.)?
2. Provide advanced techniques (Contextual Retrieval, LLM-powered ETL)?
3. Review your current context setup (provide feedback on specific prompts/workflows)?

Examples

Example 1: Solo PM Context Stuffing → Engineering

Context:

• Solo PM at early-stage startup
• Using Claude Projects for PRD writing
• Pasting entire PRDs (20 pages) + all user interviews (50 transcripts) every time
• Getting vague, inconsistent responses

Assessment:

• Symptoms: Hedged responses, normalized retries (4+ symptoms)
• Q1 (Decision): "I just want AI to understand my product" (no specific decision)
• Q2 (Persist/Retrieve): Persisting everything (no retrieval strategy)
• Q3 (Ownership): No formal owner (solo PM, ad-hoc)
• Q4 (Failure): Can't identify concrete failures for most context
• Q5 (Structure): Avoiding constraint documentation

Diagnosis: Active Context Hoarding Disorder

Intervention:

1. Immediate: Delete all context that fails Q4 test → keeps 20% of original
2. Week 1: Build constraints registry (10 technical constraints, 5 strategic)
3. Week 2: Create operational glossary (25 terms)
4. Week 3: Implement Research→Plan→Reset→Implement for next PRD

Outcome: Token usage down 70%, output quality up significantly, responses crisp and actionable.

Example 2: Growth-Stage Team with Agent Chains

Context:

• Product team with 5 PMs
• Custom AI agents for discovery synthesis
• Agent A (research) → Agent B (synthesis) → Agent C (recommendations)
• Each agent passes full context to next → context window explodes to 100k+ tokens

Assessment:

• Symptoms: Escalating token costs, inconsistent outputs (3 symptoms)
• Q1 (Decision): Each agent has clear decision, but passes unnecessary context
• Q2 (Persist/Retrieve): Mixing persistent and episodic without strategy
• Q3 (Ownership): No explicit owner; each PM adds context
• Q4 (Failure): Agents pass "just in case" context with no falsifiable failure
• Q5 (Structure): Missing Context Manifest

Diagnosis: Agent orchestration without boundaries

Intervention:

1. Immediate: Define bounded context per agent (Agent A outputs only 2-page synthesis to Agent B, not full research)
2. Week 1: Assign context boundary owner (Lead PM)
3. Week 2: Create Context Manifest (what persists, what's retrieved, what's excluded)
4. Week 3: Implement Research→Plan→Reset→Implement between Agent B and Agent C

Outcome: Token usage down 60%, agent chain reliability up, costs reduced by 50%.

Example 3: Enterprise with RAG but No Context Engineering

Context:

• Large enterprise with vector database RAG system
• "Stuff the whole knowledge base" approach (10,000+ documents)
• Retrieval returns 50+ chunks per query → floods context window
• Accuracy declining as knowledge base grows

Assessment:

• Symptoms: Vague responses despite "complete knowledge," normalized retries (2 symptoms)
• Q1 (Decision): Decisions clear, but retrieval has no intent (returns everything)
• Q2 (Persist/Retrieve): Good instinct to retrieve, but no filtering
• Q3 (Ownership): Engineering owns RAG, Product doesn't own context boundaries
• Q4 (Failure): Can't identify why 50 chunks needed vs. 5
• Q5 (Structure): Knowledge base has no structure (flat documents, no metadata)

Diagnosis: Retrieval without intent (RAG as context stuffing)

Intervention:

1. Immediate: Limit retrieval to top 5 chunks per query (down from 50)
2. Week 1: Implement Contextual Retrieval (Anthropic) — prepend explanatory context to each chunk during indexing
3. Week 2: Add metadata to documents (category, recency, authority)
4. Week 3: Product team defines retrieval intent per query type (discovery = customer insights, feasibility = technical constraints)

Outcome: Accuracy up 35% (from Anthropic benchmark), latency down 60%, token usage down 80%.

Common Pitfalls

1. "Infinite Context" Marketing vs. Engineering Reality

Failure Mode: Believing "1 million token context windows" means you should use all of them.

Consequence: Reasoning Noise degrades performance; accuracy drops below 20% past ~32k tokens.

Fix: Context windows are not free. Treat tokens as scarce; optimize for density, not volume.

2. Retrying Instead of Restructuring

Failure Mode: "It works if I run it 3 times" → normalizing retries instead of fixing structure.

Consequence: Wastes time and money; masks deeper context rot issues.

Fix: If retries are common, your context structure is broken. Apply Q5 (fix structure, don't add volume).

3. No Context Boundary Owner

Failure Mode: Ad-hoc, implicit context decisions → unbounded growth.

Consequence: Six months later, every query stuffs 100k tokens per interaction.

Fix: Assign explicit ownership; create Context Manifest; schedule quarterly audits.

4. Mixing Always-Needed with Episodic

Failure Mode: Persisting historical data that should be retrieved on-demand.

Consequence: Context window crowded with irrelevant information; attention diluted.

Fix: Apply Q2 test: persist only what's needed in 80%+ of interactions; retrieve the rest.

5. Skipping the Reset Phase

Failure Mode: Never clearing context window during Research→Plan→Implement cycle.

Consequence: Context rot accumulates; goal drift; dead ends poison implementation.

Fix: Mandatory Reset phase after Plan; start implementation with only high-density plan as context.

References

Related Skills

• ai-shaped-readiness-advisor (Interactive) — Context Design is Competency #1 of AI-shaped work
• problem-statement (Component) — Evidence-based framing requires context engineering
• epic-hypothesis (Component) — Testable hypotheses depend on clear constraints (part of context)
• pol-probe-advisor (Interactive) — Validation experiments benefit from context engineering (define what AI needs to know)

External Frameworks

• Dean Peters — Context Stuffing Is Not Context Engineering (Dean Peters' Substack, 2026)
• Teresa Torres — Continuous Discovery Habits (Context Engineering as one of 5 new AI PM disciplines)
• Marty Cagan — Empowered (Feasibility risk in AI era includes understanding "physics of AI")
• Anthropic — Contextual Retrieval whitepaper (35% failure rate reduction)
• Google — Context engineering whitepaper on LLM-powered memory systems

Technical References

• RAG (Retrieval-Augmented Generation) — Standard technique for episodic context retrieval
• Vector Databases — Semantic search for long-term memory (Pinecone, Weaviate, Chroma)
• Contextual Retrieval (Anthropic) — Prepend explanatory context to chunks before embedding
• LLM-as-Judge — Automated evaluation of context quality