The Vibe Coding Starter Kit: 16 AI-Ready Documentation Templates for Building Apps From Scratch
Stop getting broken code from AI assistants. These 16 free, copy-ready documentation templates give AI the context it needs to actually help you build working software.
Last week, I watched a developer spend three hours arguing with Claude about a simple form component. The AI kept “fixing” things that weren’t broken, forgetting context from five messages ago, and generating code that contradicted what it had written ten minutes earlier.
Sound familiar?
Here’s the thing: that developer wasn’t doing anything wrong. They were just coding the way most of us do with AI—starting fresh conversations, copy-pasting requirements, and hoping the AI would somehow remember what they were building.
It doesn’t work. And I’m convinced the problem isn’t the AI. It’s us.
The Real Problem With AI Coding
I’ve seen teams get wildly different results with the exact same AI model. Some build entire applications in days. Others spend weeks debugging AI-generated spaghetti code.
The difference? Context.
AI coding assistants are really good at generating code—when they understand what you’re building. But here’s what most developers do:
- Start a new conversation: “Build me a login form”
- Get some code
- Start another conversation: “Now add validation”
- Get conflicting code
- Start yet another conversation: “Fix these bugs”
- Get code that breaks something else
Every new conversation is a blank slate. The AI has no idea what architecture decisions you made yesterday. It doesn’t know your naming conventions, your file structure, or why you chose React over Vue. So it guesses. And its guesses often conflict with previous guesses.
The fix isn’t better prompts. It’s better documentation.
The Vibe Coding Documentation System
“Vibe coding” has become a bit of a buzzword—the idea that you can just describe what you want and AI builds it. And honestly, that works for simple scripts and one-off tools.
But for real applications? You need structure. You need memory. You need a system.
That’s what this starter kit provides: 16 documentation templates that give AI assistants everything they need to generate consistent, working code. Not more prompts—a complete context system that works across conversations, across features, across weeks of development.
When I started using this approach, my AI interactions changed completely. Instead of re-explaining my project every time, I’d just share the relevant docs. The AI understood immediately. The code worked the first time more often. And when things needed changes, the AI could trace back through decisions to understand why things were built a certain way.
The system works because it addresses the three core problems with AI coding:
- Context loss between conversations — The templates become your AI’s memory
- Inconsistent patterns and decisions — The tech design and decisions log ensure consistency
- Vague requirements leading to wrong code — The feature specs and dev tasks eliminate ambiguity
I’ve seen solo developers ship MVPs in days using this system. I’ve seen teams reduce their debug time by half. The investment is a few hours of documentation upfront; the return is measured in weeks of saved time.
The Folder Structure
Copy this structure into your project:
docs/
├── _templates/ # Base templates (what this post covers)
│ ├── README.md # Step-by-step usage guide
│ ├── prompts-guide.md # Full prompts for AI generation
│ ├── quick-prompts.md # Short prompts for Cursor/Claude Desktop
│ └── [template files]/ # All 13 template files
│
├── 00-context/ # WHY you're building (vision, assumptions)
├── 01-product/ # WHAT you're building (requirements)
├── 02-features/ # HOW features work (specs, tasks)
├── 03-logs/ # MEMORY (decisions, bugs, learnings)
└── 04-process/ # Development workflowsThe 16 Templates
Here are all the templates included in the starter kit:
Context Templates (3 files)
| Template | Purpose | When to Create |
|---|---|---|
| vision.md | Why this product exists, who it’s for, what we’re NOT building | At project start |
| assumptions.md | What we believe, risks, open questions | Alongside vision |
| system-state.md | Current tech stack, what’s built, known issues | After initial setup |
Product Template (1 file)
| Template | Purpose | When to Create |
|---|---|---|
| prd.md | User stories with acceptance criteria, requirements | After vision is complete |
Feature Templates (4 files per feature)
| Template | Purpose | When to Create |
|---|---|---|
| feature-spec.md | What the feature does, user journey, edge cases | When planning a feature |
| tech-design.md | Component architecture, data models, implementation approach | After spec is approved |
| dev-tasks.md | Atomic, AI-executable tasks (30-60 min each) | After tech design |
| test-plan.md | Unit tests, manual checklists, accessibility testing | Alongside dev tasks |
Log Templates (5 files)
| Template | Purpose | When to Update |
|---|---|---|
| implementation-log.md | What changed in code and why | After each coding session |
| decisions-log.md | Architectural decisions with rationale | When making significant choices |
| bug-log.md | Bugs, root causes, fixes | When issues are found/fixed |
| validation-log.md | Post-launch observations, user feedback | After releases |
| insights.md | Lessons learned, patterns to repeat/avoid | Ongoing |
How to Use the Templates
Step 1: Start With Your Idea
Answer these questions first:
- What problem are you solving?
- Who is your target user? (Be specific—not “everyone”)
- What are you NOT building? (Boundaries are critical)
- How will you measure success?
Step 2: Fill Out Vision.md
The vision document is your anchor. Every AI conversation should reference it. Here’s what it includes:
- Purpose - The problem and your solution
- Target Users - Specific personas with pain points
- Boundaries - What you’re NOT building
- North Star Metrics - How you measure success
- Core Principles - 3 rules that guide decisions
Step 3: Document Assumptions
Write down what you’re betting on. Every assumption should have:
- What you assume
- Risk if it’s wrong
- How you’ll validate it
Step 4: Create Your PRD
Define requirements as user stories:
“As a [user type], I want to [action] so that [benefit].”
Each story needs testable acceptance criteria.
Step 5: Plan Your First Feature
Create a folder for your first feature and fill out:
- feature-spec.md - What it does (user perspective)
- tech-design.md - How to build it (developer perspective)
- dev-tasks.md - Atomic tasks for AI to execute
- test-plan.md - How to verify it works
Step 6: Initialize Log Files
Create empty log files—you’ll update these as you build:
- implementation-log.md
- decisions-log.md
- bug-log.md
- validation-log.md
- insights.md
Using AI to Generate Templates
You don’t have to fill these out manually. The starter kit includes two prompt guides:
For AI With File Access (Cursor, Claude Desktop, Windsurf)
Use short, reference-based prompts:
Create docs/00-context/vision.md for my project.
My idea: A task manager for daily priorities
Target user: Busy professionals
Main problem: Existing tools are too complex
Not building: Team collaboration, integrations
Use template: docs/_templates/00-context/vision_template.md
Follow the LLM Instructions section.The AI reads the template file, follows the instructions, and creates a filled document.
For Web-Based AI (ChatGPT, Claude.ai)
Use the full prompts in prompts-guide.md which include all the template structure inline.
Complete Project Setup Prompt
To generate everything at once:
I'm starting a new project using the Vibe Coding system.
My project idea: [DESCRIBE IN 2-3 SENTENCES]
Target user: [WHO WILL USE THIS]
Key features:
1. [FEATURE 1]
2. [FEATURE 2]
3. [FEATURE 3]
Please read the templates in docs/_templates/ and create:
1. docs/00-context/vision.md
2. docs/00-context/assumptions.md
3. docs/01-product/prd.md
4. docs/00-context/system-state.md
5. docs/02-features/[first-feature]/feature-spec.md
6. docs/02-features/[first-feature]/tech-design.md
7. docs/02-features/[first-feature]/dev-tasks.md
8. docs/03-logs/implementation-log.md
9. docs/03-logs/decisions-log.md
Follow the LLM Instructions in each template.Example: Building a Task Manager
Let me show you how this works in practice.
Your idea: A minimal task manager focused on daily priorities.
1. Fill Out Vision (5 minutes)
Tell the AI:
“My target user is busy professionals overwhelmed by tools like Todoist. The problem is complexity—I just want to see what I need to do today. Not building: recurring tasks, team features, integrations.”
2. Generate Assumptions (3 minutes)
“Based on the vision, create assumptions.md. Key assumptions: users will use this daily, mobile-first matters, we don’t need accounts for MVP.”
3. Create PRD (10 minutes)
“Create the PRD with these features: add tasks, mark complete, set today’s priorities. P0 features only.”
4. Recommend Tech Stack (5 minutes)
“Recommend a simple tech stack. I prefer React and want local storage for MVP—no backend.”
5. Plan First Feature (20 minutes)
“Create feature docs for ‘Add Task’ feature. Simple form with task text and priority level.”
6. Start Building
Now you have atomic tasks. Open TASK-001 and tell the AI:
I'm building a task manager. Here's the context:
## Vision
[paste summary from vision.md]
## Task
[paste TASK-001 from dev-tasks.md]
Please implement this task.The AI knows exactly what you’re building, follows your patterns, and generates consistent code.
The Workflow Loop
Once you’re set up, the daily workflow is simple:
- Pick a task from dev-tasks.md
- Share context (vision + task details) with AI
- Implement and test
- Update logs:
- Mark task complete in dev-tasks.md
- Add entry to implementation-log.md
- Commit and repeat
This loop becomes second nature after a few days. The key insight is that documentation isn’t separate from development—it’s part of development. Each task update takes 2-3 minutes. The implementation log entry takes 5 minutes. This small investment compounds: after a week, you have a complete history of what was built and why. After a month, you can onboard new team members in an afternoon instead of a week.
The loop also surfaces problems early. If you find yourself struggling to write what you implemented, that’s a signal that the task wasn’t clear enough. If your implementation log entry reveals you deviated significantly from the tech design, that’s a signal to update the design. The documentation becomes a feedback mechanism for your process.
What Each Template Contains
Every template in the starter kit includes:
- Header - Purpose, when to create, update frequency
- LLM Instructions - Specific guidance for AI on how to fill it
- Template Structure - The actual template with placeholders
- Related Documents - Links to other relevant files
Placeholder Syntax
All templates use this pattern:
{{PLACEHOLDER}}- Replace with your content{{OPTIONAL}}- Can be removed if not needed{{DATE}}- Current date in YYYY-MM-DD format
Common Mistakes to Avoid
1. Skipping the Logs
Without logs, every conversation starts from zero. Update your implementation log after every session—even if it’s just “fixed a typo in Button.jsx.”
2. Vague Tasks
“Make it work” isn’t a task. “Create FormInput component with validation and error states per tech-design.md” is a task.
3. Letting Docs Go Stale
If your system-state.md says you’re using React 17 but you upgraded to 19 last month, the AI will generate outdated patterns.
4. Not Referencing Docs in Prompts
The docs only help if you share them. Start every significant AI conversation with “Based on vision.md and tech-design.md…”
Time Investment
Here’s how long each document takes:
| Document | Time | Creates |
|---|---|---|
| vision.md | 30 min | Project purpose |
| assumptions.md | 20 min | Risk awareness |
| prd.md | 1-2 hrs | Requirements |
| system-state.md | 15 min | Tech decisions |
| Feature docs (×4) | 2 hrs | Build-ready specs |
| Log files (×5) | 15 min | Project memory |
| TOTAL | 4-5 hours | Complete project plan |
ROI: This 4-5 hour investment saves 10-20+ hours of confusion, rework, and context-switching during development.
Getting the Templates
The complete starter kit includes:
Guide Files (3):
- README.md - Step-by-step usage instructions
- prompts-guide.md - Full prompts for every template
- quick-prompts.md - Short reference prompts
Template Files (13):
- 3 context templates (vision, assumptions, system-state)
- 1 product template (prd)
- 4 feature templates (spec, design, tasks, tests)
- 5 log templates (implementation, decisions, bugs, validation, insights)
Quick Reference Table
| Document | Location | Purpose |
|---|---|---|
| vision.md | 00-context/ | Why and who |
| assumptions.md | 00-context/ | Risks and unknowns |
| system-state.md | 00-context/ | Current tech stack |
| prd.md | 01-product/ | Requirements |
| feature-spec.md | 02-features/{name}/ | Feature definition |
| tech-design.md | 02-features/{name}/ | How to build |
| dev-tasks.md | 02-features/{name}/ | Atomic tasks |
| test-plan.md | 02-features/{name}/ | How to test |
| implementation-log.md | 03-logs/ | What changed |
| decisions-log.md | 03-logs/ | Why we decided |
| bug-log.md | 03-logs/ | Issues and fixes |
| validation-log.md | 03-logs/ | Post-launch data |
| insights.md | 03-logs/ | Lessons learned |
Deep Dive: What Each Template Does
Let me walk through the key templates in more detail so you understand exactly what goes in each one.
The Vision Document
This is the most important document in the entire system. I’ve seen teams struggle for weeks because they skipped this step and jumped straight into coding. The vision document forces you to answer questions you might otherwise avoid:
What problem are you actually solving? Not what features you want to build—what pain are you relieving? If you can’t articulate the problem clearly, you’ll build features that don’t connect to anything meaningful.
Who exactly is your user? “Everyone” is not an answer. Neither is “developers” or “small businesses.” You need a specific persona with a name, a profile, and a pain point you can describe in concrete terms.
What are you NOT building? This section is crucial. Most projects fail not because they didn’t build enough, but because they tried to build too much. Your boundaries should be explicit. “We will not build team collaboration features” is a decision that saves you months of scope creep.
The vision document also includes your core principles—the rules that guide every decision. When the AI asks whether to use a dropdown or a search input, your principles should make the answer obvious.
The Assumptions Document
Every project is built on bets. The assumptions document makes those bets explicit so you can track them and respond when they’re proven wrong.
I recommend organizing assumptions into three categories:
Product assumptions are things you believe about your users. “Users will check this app daily” is an assumption. “Users prefer simple over feature-rich” is an assumption. These are the beliefs that, if wrong, could invalidate your entire product strategy.
Technical assumptions are things you believe about your technology choices. “React can handle our rendering requirements” is an assumption. “Local storage is sufficient for MVP” is an assumption. These are the beliefs that, if wrong, could require significant rework.
Business assumptions (if applicable) are things you believe about the market. Timing, competition, pricing—all assumptions until validated.
Each assumption should have a validation method. How will you know if it’s true? If you can’t answer that question, you’re not making an assumption—you’re making a wish.
The PRD (Product Requirements Document)
The PRD is where you define WHAT you’re building in terms of user behavior. Every feature request should be traceable back to a user story in this document.
User stories follow a simple format that forces you to think about motivation:
“As a [user type], I want to [action] so that [benefit].”
The “so that” part is critical. It forces you to articulate WHY the user wants this feature. “As a user, I want to add tasks” is incomplete. “As a user, I want to add tasks so that I don’t forget important things” is better—now you know the underlying motivation is memory management, which might inform how you design the completion and reminder features.
Every user story needs acceptance criteria—specific, testable conditions that define “done.” If you can’t write acceptance criteria for a story, the story isn’t specific enough.
The Feature Spec
When you start working on an actual feature, the feature spec defines it from the user’s perspective. This isn’t about how you’ll build it—it’s about what the user experiences.
The feature spec includes:
User intent — Who is doing what, when, and why? What triggers this action? What’s the underlying motivation?
User journey — A step-by-step flow from trigger to success state. What happens at each step? What does the user see, do, and feel?
Acceptance criteria — The testable conditions that define success. These should map directly to the user stories in your PRD.
Edge cases — What happens when things go wrong? Empty states, error conditions, boundary scenarios. Most bugs live in edge cases you didn’t think about.
UI/UX requirements — ASCII wireframes work great here. You don’t need fancy mockups—you need a clear enough picture that implementation is unambiguous.
The Technical Design
After the feature spec is approved (meaning you know WHAT to build), the tech design defines HOW to build it.
The tech design is written for developers and AI assistants. It includes:
Component architecture — What components do you need? How do they relate to each other? I use ASCII diagrams:
TaskForm
├── TaskInput
├── PrioritySelector
└── SubmitButtonData models — What data structures do you need? Define interfaces in TypeScript or similar:
Task {
id: string
text: string
priority: 'high' | 'medium' | 'low'
completed: boolean
createdAt: ISO timestamp
}Implementation approach — Break the work into phases. What do you build first? What depends on what?
File structure — Where does each file go? Follow your project’s conventions.
The tech design is what makes AI coding actually work. When you share this document with Claude or GPT, the AI knows exactly what components to build, what interfaces to implement, and where to put the files.
The Dev Tasks
This is my favorite template. Dev tasks break the tech design into atomic, AI-executable chunks.
Each task should be completable in 30-60 minutes. That’s not arbitrary—it’s the sweet spot for AI coding sessions. Longer tasks require too much context. Shorter tasks create too much overhead.
A good task includes:
Context files — Which documents should you share with the AI for this task? Usually the tech design and maybe the system state.
Task description — Detailed instructions for what to build. Be specific. “Create the TaskInput component” is vague. “Create the TaskInput component with controlled input, placeholder text, and onChange handler that validates minimum length” is specific.
Files to create or modify — List the exact file paths. No ambiguity.
Acceptance criteria — How do you know this task is done? Testable conditions.
When you work with AI, you literally hand it a task from this document. “I’m working on TASK-003. Here’s the context…” The AI has everything it needs in one place.
The Log Templates
Logs are your project’s memory. They’re what most teams skip, and it’s why most teams struggle with AI coding over time.
Implementation log — After every coding session, document what changed. Which task did you complete? What files were modified? What decisions were made along the way? What should the next developer (or AI conversation) know?
Decisions log — When you make a significant decision, document it. What was the context? What options did you consider? Why did you choose this one? What are the consequences?
This prevents relitigating the same decisions. When someone asks “why didn’t you use Firebase?” you can point to ADR-003 where you explained the Supabase decision with full rationale.
Bug log — Track bugs with reproduction steps, investigation notes, root causes, and fixes. Include prevention measures. Without this, you’ll fix the same bug twice.
Validation log — After you release something, track how it performs. What are users doing? What feedback are you hearing? What metrics are you hitting?
Insights — Capture patterns to repeat and patterns to avoid. What worked well? What would you do differently? This becomes invaluable for future projects.
Adapting the System for Your Workflow
The templates are designed to be modified. Here’s how different teams adapt them:
Solo Developers
If you’re working alone, you might simplify:
- Combine vision and assumptions into a single document
- Skip the validation log until you launch
- Keep the implementation log minimal—just task + files changed
The must-haves for solo work: vision (boundaries!), dev-tasks (atomic work units), and decisions log (you’ll forget why you did things).
Small Teams (2-5 people)
For small teams, the full system works well. The key additions:
- Assign owners to assumptions and risks
- Review logs in weekly standups
- Use the feature docs for design reviews before implementation
Larger Teams
For larger teams, you might expand:
- Add approval workflows (Draft → In Review → Approved)
- Create templates for different feature sizes (bug fix, enhancement, epic)
- Add team-specific sections (security review, performance review)
Common Patterns I’ve Seen
After helping dozens of developers adopt this system, I’ve noticed patterns in how people use it successfully.
The “Vision First” Pattern
Successful teams spend more time on the vision than feels comfortable. If your vision document is done in 10 minutes, you probably rushed it. A solid vision document takes 30-60 minutes of genuine thinking.
The “Assumptions Debug” Pattern
When something goes wrong, check your assumptions first. “Why isn’t this working?” often leads to “Oh, we assumed X but actually Y is true.” The assumptions document should be a living document that you update weekly.
The “Small Tasks Win” Pattern
The developers who get the most value from AI break everything into small tasks. If a task takes more than an hour, it’s too big. Split it. The overhead of more tasks is worth the clarity.
The “Log Everything” Pattern
The developers who struggle least over time are the ones who actually maintain their logs. It feels tedious in the moment, but three weeks later when you can’t remember why you chose that library, you’ll be grateful.
Troubleshooting Guide
Here are problems I see and how to fix them:
Problem: AI keeps generating code that conflicts with previous code.
Solution: You’re probably not sharing enough context. At minimum, share your vision summary and the current task. For complex features, also share the tech design.
Problem: AI forgets decisions from earlier in the conversation.
Solution: Your conversations are too long. Start fresh more often, but share the relevant docs at the start. If a decision matters, add it to your decisions log and reference that.
Problem: Tasks feel too vague.
Solution: Your tech design isn’t specific enough. You should be able to trace every task directly to a component or function in the tech design.
Problem: Documentation is getting stale.
Solution: Build documentation updates into your workflow. After every coding session: update dev-tasks (mark complete), add to implementation log, update system-state if anything changed.
Next Steps
- Copy the folder structure into your project
- Fill out vision.md for your current project (or a new one)
- Create your first feature docs and break it into atomic tasks
- Try the workflow with your AI assistant of choice
The templates work with any AI coding tool—Claude, GPT, Cursor, Windsurf, GitHub Copilot. The key is giving the AI consistent, structured context.
The system is designed to be adapted—if you find sections that don’t work for your workflow, modify them. The important thing is having some structure, not this exact structure.
Happy vibe coding.
Related Reading
If you’re new to vibe coding, check out What is Vibe Coding? for the fundamentals, or dive into Vibe Coding Best Practices for advanced techniques.
For tool-specific guides, see our Cursor AI Tutorial and the complete Vibe Coding Tools Comparison.
Have questions about implementing this system? Check out Anthropic’s Claude documentation for more on working effectively with AI coding assistants, or explore OpenAI’s platform documentation for GPT integration patterns.