Vibe Coding is the practice of orchestrating AI like a modular expert ensemble: you supply precise constraints, exemplars, and validation harnesses; it produces drafts you iteratively debug. Syntax memory is optional—debugging the gap between intent and output is the core craft.
The Core Shift
Old moat: Library recall & framework rituals. New moat: Structured decomposition + assertion-led iteration. 5–7 deliberate refinement passes beat single-shot prompting.
- Primary skill: Constraint articulation (must / must-not / edge cases).
- Acceleration lever: High-fidelity exemplars (good + broken cases).
- Compounding asset: Logged iteration diffs & effective prompt patterns.
1. Interaction Loop
Loop: Spec → Draft → Validate → Delta Feedback → Refine. Mirrors the Universal AI Workflow Pattern but optimized for human-in-the-loop velocity.
2. Specification Over Prompt Blur
- Template: ROLE | OBJECTIVE | FORMAT | CONSTRAINTS | EXAMPLES | EDGE_CASES | VALIDATION_NOTES
- Convert adjectives into quantifiable criteria (< 300 tokens, JSON schema valid, 0 critical lint failures).
- Chunk multi-objective asks; one structural concern per iteration.
3. Early Validation Harness
Non-code artifacts still admit tests: required headings, banned phrases, numeric tolerance ranges, reference citation count, structural JSON schema checks. Failures feed directly as delta instructions.
4. Iteration Cadence
- Pass 0: Skeleton shape & slot inventory.
- Pass 1–2: Structural lock (section order, field set, API signatures).
- Pass 3–4: Accuracy & edge coverage.
- Pass 5+: Compression, style polish, guardrail insertion.
5. Failure Patterns
- Spec Drift: Expanding goals mid-loop without baseline freeze.
- Context Dumping: Unranked knowledge floods token budget, dilutes precision.
- Unlogged Successful Prompts: Losing compounding advantage.
- Premature Abandonment: Quitting after 1–2 imperfect drafts.
6. Minimal Playbook
- Reusable spec template file.
- Assertion checklist (structure, factual anchors, formatting constraints).
- Iteration log (Δ requested → Δ achieved).
- Exemplar library (good, borderline, failure cases).
7. When (and Why) to Add Code
Add light scripting when you need batching, orchestration, or persistent state. Even then, generate scaffolds via AI; you review semantics & tests.
Hyper‑Productivity Reality Check
10% of operators at ~100× yields ≈10× org throughput only if integration, verification, and architectural coherence scale. Leverage = specification & validation bandwidth, not raw draft flood.