Logic Granularity: A Founder’s Blueprint for Reliable Vibe-Coding Workflows

How to orchestrate AI agents, freeze complexity, and ship faster than teams ten times your size

Executive Takeaways

Why Read	What You’ll Learn
Ship faster without breaking things.	Decompose every feature into five self-contained layers and “freeze” each one once it passes its tests.
Turn AI into a dependable teammate.	Feed Large Language Models (LLMs) curated context through Model Context Protocol (MCP) and Context7, eliminating hallucinated APIs.
Run lean.	A five-person crew can now out-iterate a fifty-person team by embracing logic granularity and automated context injection.

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1. From Typing Code to Directing Code

Vibe coding is the art of building software by conversing with an LLM instead of writing every line yourself.
– Andrej Karpathy

In a typical “vibe-coded” sprint:

1. Describe the intent in plain language.
2. The model generates scaffolds, tests, and build scripts.
3. You direct and test, refining prompts rather than syntax.

Many early-stage teams now report 90 %+ AI-generated LOC. Yet speed without structure invites chaos. That’s where logic granularity comes in.

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2. Why Logic Granularity Is Non-Negotiable

A single clarifying prompt can ripple across thousands of lines—unless you place sandbags between concerns.
Logic granularity slices the journey from idea ➜ production into five verifiable layers. Once a layer is accepted, it is locked; downstream edits cannot rewrite upstream contracts.

Failing to isolate layers leads to:

• Undetected regressions (UI tweak breaks back-end schema).
• Prompt drift (later requests overwrite earlier assumptions).
• Tech-debt snowballs (the model “helpfully” rewrites stable code).

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3. The Five-Layer Stack

Layer	Primary Deliverable	“Definition of Done”	Example Prompt
1. Concept	One-sentence value prop	Passes elevator-pitch test	“A tool that publishes newsletters in 30 seconds.”
2. PRD	Features + KPIs	Formal PRD accepted by PMs	“Draft an MVP PRD deliverable in two weeks.”
3. Flow	Wireframes & user stories	Happy-path wireflow reviewed	“Translate the PRD into labeled wireflows.”
4. UI/UX Components	Design tokens + Storybook specs	Visual review signed off	“Design primary components in Tailwind & shadcn/ui.”
5. Code	Business logic + tests	CI green; coverage ≥ 90 %	“Implement Button.tsx with variants and Jest tests.”

Workflow in Action

Freeze gates ensure a downstream change can’t invalidate an upstream promise.

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4. Lock Reality into the Prompt (MCP + Context7)

Even layered prompts can hallucinate outdated APIs. Model Context Protocol (MCP) acts as a USB-C port for truth:

1. Annotate your prompt
   use context7@tailwindcss@^3.4
2. LLM fetches authoritative docs in milliseconds.
3. Generated code now imports bg-primary/90, not the retired btn-primary.
4. Tests validate; layer locks.

Result: zero hand-copied documentation, zero stale imports.

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5. A Lightweight “Build in Public” Pipeline

1. Publicly declare the Concept
   Tweet your elevator pitch; collect feedback in hours, not weeks.
2. Open-source the PRD
   Show your roadmap; invite issue-track votes.
3. Stream the Wireflow review
   Figma links + live commentary = instant user empathy.
4. Share the Storybook
   Readers can play with components before you write production code.
5. Green-badge your CI runs
   Every passing build becomes marketing proof.

Transparency doubles as free QA.

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6. Leadership Playbook

Discipline	Old-School Playbook	Logic-Granular Playbook
Product	Monolithic PRD → dev hand-off	PM owns Layers 1–2; can pivot without touching code.
Design	Pixel-perfect Figma, then “throw over wall”	Designers govern Layers 3–4; frozen tokens mean no surprise colour shifts.
Engineering	Write everything; fight scope creep	AI writes 80–90 %; engineers curate Layer 5 and safeguard tests.
Ops	Manual doc updates	MCP + Context7 auto-inject docs at build time.
Team Size	15–50 for MVP	5 people can now ship investor-ready demos.

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7. Guardrails & Gotchas

Risk	Mitigation
Prompt entropy – the model forgets earlier agreements.	Add a JSON schema contract to each prompt; test for compliance.
Silent regressions from large diff bursts.	Freeze a layer once its tests pass; any change must add new tests.
Vendor lock-in for context servers.	Context7 is open source; self-host if compliance demands.
Model updates change behaviour.	Pin model versions per layer; treat upgrades as new sprints.

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8. Getting Started This Weekend

1. Pick a bite-sized concept you can explain in < 15 words.
2. Write one acceptance test before generating any code.
3. Add MCP annotations for every external library.
4. Iterate layer-by-layer; freeze aggressively.
5. Publish a build-log thread; let the crowd find your blind spots.

You’ll feel slower at first—then notice you’re shipping features while rivals rewrite specs.

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Conclusion

Logic granularity is not a buzzword; it is the missing operating system for AI-augmented product teams. Pair it with MCP-powered context and a habit of freezing layers, and you unlock a flywheel:

Clear intent → Verified layer → Automated context → Stable code → Faster experiments

Adopt this mindset on your next side project. You may discover that letting AI take the keyboard frees you to take the helm.

Happy building—in public, at warp speed.