Advanced AI Coding - Context Engineering and Spec-Driven Development

You’ve tried vibe coding and seen its potential. Now you’re wondering how to go from generating simple functions to building complex features with AI assistance. The difference between beginners and power users comes down to two key skills: context engineering and spec-driven development.

Beyond Basic Prompts

Most developers start with AI by typing simple requests like “write a login function.” This works for isolated snippets, but falls apart when building real software. Why? Because AI doesn’t know:

Your project’s architecture and patterns
Your team’s coding conventions
The business logic and constraints
How components interact with each other

Advanced AI-assisted development is about systematically providing this information.

What is Context Engineering?

Context engineering is the practice of deliberately structuring and providing information to AI systems to get better, more relevant outputs. Think of it as the difference between giving someone directions by saying “go that way” versus providing a detailed map with landmarks.

Context has several dimensions:

1. Codebase Context The AI needs to understand your existing code structure, patterns, and dependencies.

2. Requirements Context What problem are you solving? What are the constraints and edge cases?

3. Architectural Context How does this piece fit into the larger system? What patterns should it follow?

4. Historical Context What decisions were made before? What was tried and didn’t work?

Practical Context Engineering Techniques

Project Documentation Files

Create structured files that AI tools can reference. Many AI coding tools look for specific files in your project root:

# CLAUDE.md (or similar for other tools)
 
## Project Overview
This is a NestJS backend for an e-commerce platform using PostgreSQL
and Redis for caching.
 
## Architecture
We follow hexagonal architecture with these layers:
- /src/domain - Business entities and interfaces
- /src/application - Use cases and services
- /src/infrastructure - Database, external APIs, frameworks
 
## Coding Conventions
- Use dependency injection for all services
- Repositories return domain entities, not ORM models
- All public methods must have JSDoc comments
- Error handling uses custom domain exceptions
 
## Testing Strategy
- Unit tests for domain logic
- Integration tests for repositories
- E2E tests for critical user journeys

This file acts as persistent context that informs every AI interaction.

Structured Prompt Templates

Instead of ad-hoc prompts, create templates for common tasks:

## Feature Implementation Template
 
### Context
- Feature: [name]
- Related files: [list existing files AI should reference]
- Dependencies: [external services, libraries involved]
 
### Requirements
- [Requirement 1]
- [Requirement 2]
 
### Constraints
- Must work with existing [X] system
- Performance requirement: [specify]
- Security considerations: [specify]
 
### Expected Output
- Files to create/modify: [list]
- Tests required: [specify]

Layered Context Strategy

Provide context in layers, from broad to specific:

Layer 1 (Project): "This is a TypeScript monorepo with shared packages"
Layer 2 (Domain): "We're working in the payments module"
Layer 3 (Feature): "Implementing refund processing"
Layer 4 (Task): "Write the validation logic for partial refunds"

Each layer narrows the focus while maintaining necessary background.

Spec-Driven Development with AI

Spec-driven development means writing detailed specifications before generating code. This approach dramatically improves AI output quality.

The Specification Document

Before asking AI to write code, create a specification:

# Specification: Order Cancellation Service
 
## Purpose
Handle order cancellation requests, including inventory restoration,
payment refunds, and notification dispatching.
 
## Interface
 
### Input
```typescript
interface CancelOrderRequest {
  orderId: string;
  reason: CancellationReason;
  requestedBy: UserId;
  partialCancellation?: {
    itemIds: string[];
  };
}

Output

interface CancelOrderResult {
  success: boolean;
  refundAmount?: Money;
  restoredItems?: InventoryItem[];
  errors?: CancellationError[];
}

Business Rules

Orders can only be cancelled within 24 hours of placement
Shipped items cannot be cancelled, only returned
Partial cancellations must refund proportionally
Inventory must be restored before refund is processed

Error Scenarios

Order not found → NotFoundError
Order already shipped → ShippedOrderError
User not authorized → UnauthorizedError
Refund service unavailable → retry with exponential backoff

Dependencies

OrderRepository: fetch and update orders
InventoryService: restore stock
PaymentGateway: process refunds
NotificationService: send confirmations

Testing Requirements

Unit tests for each business rule
Integration test for full cancellation flow
Error scenario coverage


Now when you ask AI to implement this, it has everything needed to generate production-quality code.

### Skills-Based Development

Some AI tools support "skills"—predefined capabilities that can be invoked for specific tasks. Even without built-in support, you can create your own skill system.

#### Defining Custom Skills

Create a skills directory with reusable prompts:

```text
/ai-skills
  /code-review.md
  /api-design.md
  /test-generation.md
  /security-audit.md

Example skill definition:

# Skill: API Design Review
 
## Trigger
Use when designing or reviewing REST API endpoints.
 
## Checklist
1. Resource naming follows REST conventions
2. HTTP methods match operations (GET=read, POST=create, etc.)
3. Status codes are appropriate
4. Error responses include actionable messages
5. Pagination implemented for list endpoints
6. Rate limiting considered
7. Authentication/authorization specified
8. Request/response schemas documented
 
## Output Format
Provide analysis in this structure:
- Compliance: [score/10]
- Issues found: [list]
- Recommendations: [list]
- Example improvements: [code samples]

Composing Skills

Complex tasks often require multiple skills in sequence:

Task: Implement new payment endpoint

Skills to apply:
1. [API Design] - Design the endpoint contract
2. [Security Audit] - Review for vulnerabilities
3. [Code Generation] - Implement the endpoint
4. [Test Generation] - Create test coverage
5. [Documentation] - Generate API docs

Advanced Prompting Patterns

The Architect Pattern

Ask AI to think at the architecture level first:

Before writing any code, analyze this feature request and provide:

1. Component breakdown - what modules/classes are needed
2. Data flow - how information moves through the system
3. Integration points - where this touches existing code
4. Risk assessment - what could go wrong
5. Implementation sequence - optimal order of development

Only after I approve this analysis, proceed to implementation.

The Reviewer Pattern

Have AI critique before finalizing:

Review the code you just generated for:
- Edge cases not handled
- Potential performance issues
- Security vulnerabilities
- Violations of our coding standards
- Missing error handling

Then provide an improved version addressing these issues.

The Adversarial Pattern

Ask AI to find problems:

You are a senior developer reviewing this code for a production system
that handles financial transactions. Be critical and thorough.

What bugs, security issues, or design problems do you see?
What would you reject in a code review?

Building a Context Library

Over time, build a library of reusable context documents:

/ai-context
  /architecture
    system-overview.md
    data-model.md
    integration-patterns.md
  /standards
    coding-conventions.md
    security-requirements.md
    testing-standards.md
  /domain
    business-glossary.md
    domain-rules.md
  /templates
    feature-spec.md
    bug-fix-template.md
    refactoring-plan.md

Reference these in your prompts:

Following the patterns in /ai-context/architecture/integration-patterns.md
and the security requirements in /ai-context/standards/security-requirements.md,
implement the OAuth integration specified below...

Measuring Effectiveness

Track your AI-assisted development to improve over time:

Metrics to consider:

Acceptance rate: How often do you use generated code as-is vs. heavily modify?
Iteration count: How many prompts to get acceptable output?
Bug rate: Do AI-generated sections have more defects?
Time savings: Compare similar tasks with and without AI

Improving based on metrics:

Low acceptance rate → Improve specification detail
High iteration count → Better context documents needed
Higher bug rate → Add more review steps
Minimal time savings → Rethink which tasks benefit from AI

The Compound Effect

The real power emerges when these techniques compound:

Specifications define what to build clearly
Context engineering ensures AI understands your system
Skills provide consistent, high-quality approaches
Review patterns catch issues before they ship

Each element reinforces the others. A good specification makes context more focused. Strong context makes skills more effective. Thorough reviews improve future specifications.

Practical Implementation Checklist

Ready to level up? Here’s your action plan:

Conclusion

Moving from basic vibe coding to advanced AI-assisted development is about structure and intentionality. Context engineering ensures AI understands your world. Spec-driven development defines what success looks like. Skills create repeatable quality.

The developers who master these techniques don’t just use AI—they orchestrate it. They spend less time fighting with prompts and more time building software.

Start with one technique. Add context to your project. Write one specification. Create one skill. Build from there. The compound effect will transform how you work with AI.

The goal isn’t to generate more code faster. It’s to generate the right code, the first time, that fits perfectly into your system.