Why Specs Beat Vibe Coding
You ask Claude to write a report about personal AI employees. It generates a draft. You realize you wanted more focus on business ROI, so you ask for that. Claude rewrites—but now the technical details you liked are gone. You clarify, and Claude apologizes and adds them back—but the structure is completely different. Twenty minutes later, you have a document that technically addresses your topic but reads like three different authors with conflicting views.
Sound familiar?
This is vibe coding—the natural way people first approach AI assistants. You have a vague idea, you describe it, and AI generates something. You refine through conversation. Sometimes it works brilliantly. Other times, you spend more time correcting than you would have spent writing it yourself.
The problem is not the AI. The problem is the workflow. Vibe coding fails systematically for substantial work, and understanding why reveals the solution.
The Vibe Coding Pattern
Vibe coding follows a predictable cycle:
Prompt → Output → "No, I meant..." → Output → "Actually..." → Output → Repeat
Each iteration seems like progress. You're getting closer to what you want. But something corrosive happens beneath the surface.
Consider this real conversation (simplified for clarity):
Turn 1: "Write a report about personal AI employees in 2026."
Claude generates a broad overview covering tools, use cases, and future predictions.
Turn 5: "Focus more on business ROI and cost savings."
Claude rewrites with heavy business focus, but drops the specific tool comparisons you found useful.
Turn 9: "Bring back the tool comparisons, but keep the ROI focus."
Claude attempts to merge both, but the structure becomes inconsistent—some sections are technical, others are business-focused.
Turn 14: "Why does this read like a blog post? I needed something more like a research report with citations."
Claude apologizes and restructures, but the ROI analysis you refined is now buried in an appendix.
Turn 20: You give up and start over.
What went wrong? Not any single turn—each response was reasonable given the question asked. The failure is structural.
Three Failure Modes
Vibe coding fails in three predictable ways. Once you recognize these patterns, you'll see them everywhere.
1. Context Loss: Each Iteration Loses Discoveries
When you ask Claude to add ROI focus in Turn 5, it focuses on that request. The nuances you established in earlier turns—the specific tools you wanted compared, the technical depth you appreciated, the structure that was working—fade from attention. Not because Claude forgot (they're technically still in context), but because newer information receives more weight.
The mechanism: Each new request shifts focus. Previous decisions become background noise. With enough turns, early requirements are effectively invisible.
What it looks like:
- Content that worked gets rewritten after unrelated changes
- Decisions you made early get silently reversed later
- You find yourself re-explaining the same constraints
2. Assumption Drift: Reasonable Guesses Diverge from Intent
You said "report about personal AI employees." Claude reasonably assumed:
- You wanted a broad overview for general readers
- Blog-style format would be accessible
- Predictions about the future would be interesting
Each assumption is defensible. A thoughtful writer might make the same choices. But you needed a research report for technical decision-makers. Your audience wants citations and evidence. You need specific tool comparisons, not general predictions.
The mechanism: Without explicit constraints, AI fills gaps with reasonable defaults. Those defaults compound, and by Turn 10, you're editing a document written for an audience you don't have.
What it looks like:
- Output technically addresses your topic but feels wrong
- You discover structural decisions you didn't ask for
- Fixes require understanding an approach you didn't choose
3. Pattern Violations: Generated Output Ignores Your Standards
Turn 14 reveals the deepest problem. Claude wrote blog-style content because that's a reasonable pattern for "reports." But your work has specific standards: research reports have methodology sections, claims need citations, structure follows academic conventions.
The mechanism: Without knowledge of your specific standards, AI follows general best practices. Those practices may directly contradict your requirements.
What it looks like:
- Output works but doesn't fit your standards
- Colleagues question why the format is inconsistent
- "Fixing" requires substantial restructuring
The Compounding Problem
These three modes don't just occur—they amplify each other.
Context loss means you stop re-explaining constraints. Assumption drift means Claude fills those gaps with defaults. Pattern violations mean the defaults conflict with your architecture. By Turn 15, you're not iterating toward your goal. You're managing an increasingly divergent codebase.
| Turn | Context Loss | Assumption Drift | Pattern Violations |
|---|---|---|---|
| 1-5 | Minimal | Beginning | Undetected |
| 6-10 | Noticeable | Compounding | Emerging |
| 11-15 | Significant | Structural | Blocking |
| 16+ | Critical | Architectural | Requires rewrite |
The further you go, the harder correction becomes. This is why vibe coding works for simple tasks but fails for complex ones.
The Context Engineering Connection
In Chapter 4, you learned that context quality determines agent reliability. The three vibe coding failure modes are actually context engineering failures:
| Failure Mode | Context Engineering Problem |
|---|---|
| Context loss | Violates persistence principle (Ch 4, Lesson 7) |
| Assumption drift | Missing constraints in context (Ch 4, Lesson 5) |
| Pattern violations | Architecture not in working memory (Ch 4, Lesson 2) |
Specifications solve these by applying context engineering systematically—front-loading the context Claude needs rather than discovering it through iteration. The spec becomes the persistent, high-signal context that Chapter 4 taught you to build.
The Insight: Claude Needs the Complete Picture Upfront
The solution is surprisingly simple once you see the problem clearly.
Claude can write your report correctly on the first try—if it knows:
- What exists: Your current tables, patterns, and architecture
- What to build: Specific requirements, not vague features
- What NOT to build: Explicit constraints and boundaries
- How to validate: Success criteria that can be verified
The pattern for all three failure modes is the same: information Claude needed but didn't have. Context loss happens because requirements weren't written down. Assumption drift happens because constraints weren't explicit. Pattern violations happen because existing architecture wasn't communicated.
The specification captures this information once, upfront.
| Failure Mode | Prevented By |
|---|---|
| Context loss | Written requirements that persist across turns |
| Assumption drift | Explicit constraints that eliminate guessing |
| Pattern violations | Architecture documentation that defines patterns |
This is the foundation of Spec-Driven Development: front-loading the information Claude needs rather than discovering it through iteration.
Try With AI
Running Example: Throughout this chapter, you'll write a real report: "Personal AI Employees in 2026." This lesson lets you experience why vibe coding fails for this task.
Prompt 1: Experience the Problem
Write a report about personal AI employees in 2026—tools like
Claude Code, Cowork, and similar systems that function as
digital teammates.
After Claude generates the draft, follow up: "Focus more on business ROI," then "Add specific tool comparisons," then "Make it more like a research report with citations." Notice how each iteration changes or loses earlier decisions.
What you're learning: Each clarification shifts focus. By the third iteration, the structure you liked may have disappeared. The tool comparisons get buried. This is the vibe coding failure pattern in action.
Prompt 2: Surface the Assumptions
Look at the report you just wrote. List every assumption you made:
- Who is the target reader?
- What do they already know about AI tools?
- What format did you choose and why?
- What did you emphasize vs skip?
For each assumption, what should you have asked me first?
What you're learning: Claude made dozens of implicit decisions—general audience vs technical, blog format vs research report, breadth vs depth. These assumptions, not your topic, caused the iteration cycles. The questions Claude identifies are the outline of a specification.
Prompt 3: Preview the Solution
If I had given you a specification upfront:
- Audience: CTOs evaluating AI tools for their teams
- Format: Research report with executive summary
- Required sections: Tool comparison, ROI analysis, implementation risks
- Constraints: 2000 words, include citations, no speculation
How would your first draft have been different?
What you're learning: The specification you DIDN'T write caused the iteration. With constraints (no speculation), audience (CTOs), and format (research report) defined upfront, Claude's first draft would have matched your needs. This is why specs beat vibe coding.