AI-Powered Coding: Vibe Coding, Context Window and Spec Driven Development

AI-powered coding tools have transformed modern software development. Tools like Claude Code, GitHub Copilot, and Cursor have multiplied developer productivity. However, to maximize these tools, you must understand the vibe coding approach, context window management, and proper workflow strategies. This guide explains the fundamental principles of working professionally with LLM-based coding assistants.

Context Window: AI's Short-Term Memory

The most critical limitation of AI coding assistants is context window size. Understanding what this means is the first step to efficient work.

LLM models work in a stateless manner—they don't remember you. With each new query, the entire conversation history is resent to the AI server. The context window includes not just messages, but all operation data like file reads, terminal outputs, lint results, and web searches. This makes the AI appear to "remember" conversation context—but it's an illusion.

Performance drops as the context window approaches fullness. Proactive management is essential before it fills up.

What Happens When Context Window Fills?

• Information from the beginning of the conversation gets lost
• Hallucination rate increases
• Inconsistent and erroneous responses are given
• Previous instructions are ignored
• Code quality decreases

Why Can't You Write the Entire App with One Prompt?

A common mistake: writing a very detailed, long prompt to the AI and asking it to create the entire application in one go. This approach almost always fails.

1. Context Window Limit

While not impossible, writing a complex application in a single prompt is highly inefficient. As the project grows, context fills up, performance drops, errors increase.

2. Error Cumulative Effect

Small mistakes made in long sessions cause chain reactions. A 5-line error at step one can turn into a 500-line rewrite at step 50.

3. Context Erosion

In long conversations, AI "forgets" the project's initial requirements. You request Next.js at the start, 50 messages later it's writing a React app.

Solution: Task-Based Workflow Approach

The secret to successful vibe coding: divide project into logical tasks, focus on each task, keep context clean.

1. Break Large Tasks into Atomic Parts

Each task should focus on a single responsibility. Example: User authentication system

• Task 1: User model and database schema
• Task 2: POST /api/register endpoint (validation + bcrypt)
• Task 3: POST /api/login endpoint (JWT token)
• Task 4: Token refresh mechanism
• Task 5: Frontend register/login forms
• Task 6: Error handling and security headers

2. Each Task = New Context Window

When a task is complete, start a new conversation. Advantages:

• Clean, optimized context
• Only relevant information (previous task results)
• Zero risk of error chaining
• Maximum AI performance

3. Transfer Context for Each New Task

In the new conversation, specify:

• Project summary: "E-commerce site with Next.js 14"
• Completed tasks: "User model and register endpoint ready"
• Current task: "We'll add login endpoint"
• Technology stack: "TypeScript, Prisma, JWT, bcrypt"

Spec Driven Development: More Structured Approach

Some developers prefer a method similar to test-driven development (TDD): Spec Driven Development. Write an executable specification first, then have the AI write code that adheres to this spec.

Popular tools:

• Agent OS - Spec-driven AI agent operating system
• BMAD - 19+ agents, 50+ workflows
• Spec Kit - GitHub's specification tool
• Open Spec - Open source spec management

Warning: May be over-engineering for small projects. A simple task list is often sufficient.

Context Window Optimization Strategies

Think of context as a CPU—a limited resource. Use it efficiently with these strategies:

1. Maximum Results with Minimal Messages

Each prompt and response fills the context. Use 1 well-written prompt instead of a 10-message conversation.

Bad approach:

• "Make API"
• "Add user"
• "Add password"
• "Add validation"

Good approach:

"Write Express.js POST /api/register endpoint. Validate email/password, hash with bcrypt, return JWT token, save to PostgreSQL with Prisma."

2. Context Refresh Commands

When context fills up or performance drops:

• /clear - Start completely new conversation
• /compact - Get conversation summary, start new session
• Re-specify important info (stack, architectural decisions) in new session

3. Monitor Context Usage

AI tools display context fill rate. Track proactively for management:

• Claude Code: /context command
• Gemini CLI: /memory command
• Consider new conversation at 70-80% fullness
• Reset more frequently for critical tasks

4. Use MCP Servers Wisely

MCP servers give AI superpowers but also consume context. When each MCP server starts, schema information loads into context and stays there permanently.

Efficient MCP usage:

• Only load MCPs you use
• Disable unused servers
• Use different MCP configs per project
• Prefer lazy loading (load on demand)

Detailed info: Efficient MCP Server Usage Guide

5 Golden Rules for Vibe Coding Success

1. Manage Context Like RAM

Context is a limited resource. Every file read, web search, terminal output consumes context. Don't load unnecessary info.

2. Single Responsibility: One Task One Job

❌ "Build user system and payment integration"
✅ Task 1: "User system", Task 2: "Payment integration"

3. Test Before Moving Forward

Test after each task completes. AI code isn't 100% correct. Run build, lint, unit tests.

4. Prompt Engineering: Few Words, Much Info

Write clear, specific, contextual prompts:

• ❌ "Make an API"
• ✅ "Express.js, user registration, POST /api/register, email+password validation, bcrypt hash, return JWT token, Prisma PostgreSQL"

5. One Commit Per Task

Task completed → commit. Small commits = easy rollback, clear history.

Detailed info: Big Gains with Small Commits

3 Critical Mistakes and Solutions

❌ Mistake 1: Marathon Conversation

Problem: Writing the entire project in a 100+ message conversation

Solution: Refresh every 50-100 messages. Start new conversation per feature.

❌ Mistake 2: Context-Free Start

Problem: Saying "Make login endpoint" in new conversation (How would AI know?)

Solution: Give mini brief in each new session: "Next.js 14, TypeScript, Prisma, PostgreSQL. User model ready. Now adding login endpoint."

❌ Mistake 3: Test-Free Coding

Problem: AI wrote code, continuing without testing

Solution: Don't move to new task without running build + lint + test. Errors not caught early become cascading disasters.

Conclusion: AI is a Tool, Not a Magic Wand

AI coding assistants have democratized software development. But strategy is essential for maximum efficiency:

• Manage context window wisely - It's a limited resource
• Break tasks into atomic parts - Each task = 1 responsibility
• Test, commit, refresh - Prevent error accumulation
• Learn prompt engineering - Few messages, much info
• Optimize MCPs - Don't load unnecessary servers

Golden rule: AI + Good software engineering practices = 10x productivity. AI alone isn't enough—you must think strategically.

More resources:

• Efficient MCP Server Usage
• Big Gains with Small Commits
• Beginner-Level Git Usage