Context Engineering for Vibe Coders: Stop AI from Guessing

TL;DR: "Vibe coding" feels productive until AI starts inventing. Context engineering is the discipline of structuring project knowledge so AI tools have reliable ground truth.

Table of Contents

1. Why Vibe Coding Breaks Down
2. What is Context Engineering?
3. The Context Hierarchy
4. Context Sources Ranked
5. Building Your Context Kernel
6. Feeding Context to AI Tools
7. Common Mistakes
8. Context Maintenance
9. Free Resources

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Why Vibe Coding Breaks Down

Vibe coding = prompting AI with loose descriptions and iterating until it works.

It feels fast at first:

Day 1: "Build a todo app" → Working prototype in 2 hours!
Day 2: "Add user auth" → Works after 3 attempts
Day 5: "Add team sharing" → Breaking existing features
Day 10: "Why is nothing working anymore?"

The Breaking Point

Vibe coding fails around:

• 1,000 LOC: Too much code for AI to track
• 3+ features: Interactions cause conflicts
• Any team: Different vibes, different outputs

Root Cause

AI tools have context windows, not project knowledge. Each prompt starts fresh. Without persistent context, they reinvent patterns, forget conventions, and contradict previous code.

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What is Context Engineering?

Context engineering is the discipline of:

1. Capturing project knowledge in structured documents
2. Organizing documents into a hierarchy
3. Loading the right context for each AI interaction
4. Maintaining context as the project evolves

The Context Equation

Reliable AI Output = Relevant Context + Clear Task

Without context: AI guesses. With context: AI implements.

Context vs Prompting

Prompting	Context Engineering
One-shot instructions	Persistent knowledge base
Hopes AI remembers	Explicitly loads what's needed
Different output each time	Consistent patterns
Breaks at scale	Scales with project

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The Context Hierarchy

Context layers from global to specific:

┌─────────────────────────────────────────────┐
│ Level 5: Task Context                       │
│ (specific file, specific function)          │
├─────────────────────────────────────────────┤
│ Level 4: Implementation Specs               │
│ (API spec, schema, component inventory)     │
├─────────────────────────────────────────────┤
│ Level 3: Feature Specs                      │
│ (PRD, user stories, acceptance criteria)    │
├─────────────────────────────────────────────┤
│ Level 2: Project Context                    │
│ (architecture, tech stack, patterns)        │
├─────────────────────────────────────────────┤
│ Level 1: Global Rules                       │
│ (coding standards, conventions, principles) │
└─────────────────────────────────────────────┘

What Each Level Provides

Level	Contains	Prevents
L1: Global	Coding standards, naming conventions	Style inconsistencies
L2: Project	Architecture, tech stack, patterns	Wrong integrations
L3: Feature	PRD, scope, acceptance criteria	Scope creep
L4: Implementation	API spec, schema, components	Hallucinated APIs
L5: Task	File context, function context	Wrong references

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Context Sources Ranked

Not all context is equal. Ranked by reliability:

Rank	Source	Reliability	Why
1	Spec documents	⭐⭐⭐⭐⭐	Authoritative, maintained
2	Actual code	⭐⭐⭐⭐	Real but may be wrong
3	Comments	⭐⭐⭐	Often outdated
4	Commit messages	⭐⭐	Context-light
5	Conversation memory	⭐	Volatile, summarized
6	AI's training data	⭐	Generic, not your project

Key Insight

If you don't provide explicit context (ranks 1-4), AI falls back to its training data (rank 6). That's when hallucinations happen.

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Building Your Context Kernel

The context kernel is the core set of documents that define your project:

Minimum Viable Kernel

1. AGENTS.md — Operating rules for AI agents
2. prd.md — What you're building, what you're not
3. architecture.md — How components connect
4. api-spec.yaml — Endpoint contracts
5. schema.md — Database structure

Kernel Structure

your-project/
├── docs/
│   ├── AGENTS.md        # AI operating rules
│   ├── prd.md           # Product requirements
│   ├── architecture.md  # System design
│   ├── api-spec.yaml    # Endpoints
│   ├── schema.md        # Database
│   └── tech-stack.md    # Technologies
├── .cursor/rules/       # Cursor-specific
├── .clinerules          # Cline-specific
└── src/

Example: AGENTS.md

## AI Operating Rules

## Context Loading

Before implementing any feature:

1. Load `/docs/prd.md` for scope
2. Load `/docs/architecture.md` for patterns
3. Load relevant spec (API or schema)

## Implementation Rules

- ONLY implement features in the PRD
- ONLY use endpoints in api-spec.yaml
- ONLY query columns in schema.md
- Follow patterns in architecture.md

## When Uncertain

- Ask for clarification
- Reference the spec section that's unclear
- Do NOT invent solutions

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Feeding Context to AI Tools

Cursor

1. Add rules to .cursor/rules/
2. Use @file references in prompts
3. Enable "include open files"

Using @docs/api-spec.yaml and @docs/prd.md:
Implement the POST /api/projects endpoint.

Real-World Example: Enforcing Tech Stack

Create .cursor/rules/tech-stack.mdc to stop common hallucinations:

---
description: Tech Stack & Coding Standards
globs: **/*.ts, **/*.tsx
---

# Tech Stack Standards

- **Framework**: Next.js 15 (App Router)
- **UI**: Tailwind CSS + Shadcn/UI
- **State**: Server Actions (No API routes unless external)
- **Database**: Supabase (PostgreSQL)

## Critical Rules

1. ALWAYS use `lucide-react` for icons. NEVER use `lucide`.
2. ALWAYS use `const` components, never `function`.
3. NEVER assume columns exist. Check `@schema.md`.

Cline

1. Create .clinerules with context loading instructions
2. Use explicit file references in prompts
3. Create task templates

Context: /docs/schema.md, /docs/api-spec.yaml
Task: Create the project creation flow
Constraints: Only use defined endpoints and columns

v0 / Lovable

1. Paste relevant spec sections in prompts
2. Include component inventory for UI work
3. Reference specific acceptance criteria

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Common Mistakes

Mistake 1: Context in Head, Not in Docs

❌ "I know what the API should look like" ✅ Document it in api-spec.yaml

Mistake 2: Docs in Notion, Code in Repo

❌ Context split across tools ✅ Docs in same repo, referenced in prompts

Mistake 3: Generic Rules

❌ "Write clean code" ✅ "Use camelCase for functions, PascalCase for components"

Mistake 4: No Non-Goals

❌ PRD only says what to build ✅ PRD explicitly lists what NOT to build

Mistake 5: Never Updating Context

❌ Wrote specs once, forgot them ✅ Update specs when implementation reveals changes

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Context Maintenance

Context decays. Here's how to keep it fresh:

Update Triggers

Event	Update Action
New endpoint added	Update api-spec.yaml
Schema migration	Update schema.md
Architecture change	Update architecture.md
Feature shipped	Mark complete in PRD
Feature cut	Move to non-goals

Weekly Review

• Are specs matching implementation?
• Any new patterns to document?
• Any deprecated patterns to remove?

Validation

Periodically validate with:

@all-specs - Is this implementation correct per our specs?

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Free Resources

Templates

• SDD Template Pack — Full context kernel
• PRD Template — Scope + non-goals
• Architecture Template — System design

Guides

• Spec-Driven Development
• How to Stop Hallucinations
• Build Without Hallucinations

Tools

• Spec Readiness Score — Check your context coverage
• PRD Generator — Generate your first PRD for free
• Cursor Rules Generator — Create .cursorrules for your tech stack
• Scope Lock Wizard — Split MVP from Post-MVP features

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Build your context kernel in minutes. Generate from a brain dump →

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Last updated: January 2026