Context Engineering (3/3) - Practical Context Engineering for Vibe Coding with Claude Code

In this blog, we will take a practical example of how we can use context engineering to get the right results.

Context engineering is becoming an emerging and critical skill for Vibe Coding, as it brings structure and reliability to what was previously an intuitive but inconsistent approach to AI-assisted development. Some of the most widely used agentic AI applications include Claude Code and Gemini CLI, which assist in generating code and building complete applications. These are the true Vibe-Coding tools, which allow the user to generate complete code with zero-code experience.

Context engineering is being called “10x better than prompt engineering and 100x better than vibe coding” by Analytics India Magazine, Geeky Gadgets because it takes the creative, intuitive aspects of vibe coding and gives them a systematic foundation.

In this blog, I will share my experiences working with agentic AI applications and why I strongly feel context engineering is very critical to getting the right result. Specifically, this is a very critical skill while using tools like Claude Code or Gemini CLI to generate code.

I had covered my experiences with vibe coding in the following blogs. Please read them.

• Vibe Coding (Agentic Coding)—My experiences (1/2)

• Vibe Coding (Agentic Coding)—MCP powers the Vibe (2/2)

I created a 2-part series on context engineering. Please also go through this before going forward

• Context Engineering (1/2)—Getting the best out of Agentic AI Systems

• Context Engineering (2/2)—Product Requirements Prompts

In my experience, context engineering offers a structured approach to vibe coding and has significantly increased output quality, making the results more reliable. Here are some of my learnings:

• Eliminates “trial-error” style prompt engineering: Instead of endlessly tweaking every word, hoping your prompt would work, you provide better context through examples, summaries, memory, and clear instructions. This approach surpasses the iterative process of purely intuitive coding.

• Enables Reusability You can experiment and find what works once, then systematically incorporate that success into system prompts, user profiles, and data formats. This approach makes the magic happen consistently across multiple interactions. In fact, I am able to get similar results across other equally good models. It's also consistent.

• Adds Production-Ready Structure Context engineering creates AI agents that understand your repository, grasp your architecture, and intelligently reuse components. Code doesn’t just work—it fits seamlessly into your existing systems. Context engineering helps us to incorporate tests and security considerations and any other non-functional requirements.

• Improves Consistency and Scale By carefully defining the information environment, context engineering creates consistent, predictable outcomes.

In the context of building serious enterprise-ready, production-grade systems, context engineering brings in better accuracy, fewer production failures, and sustainable AI systems that scale. The approach helps by:

• Memory Management: Context engineering optimizes the AI’s context window by deciding what information matters most, trimming noise, and structuring data effectively. Pure vibe coding often floods the context window with irrelevant information.

• Better Architecture: Context engineering is closer to designing software than writing prompts. You’re building a system that works across many interactions, over time, and across various inputs—not just getting a single response.

• Quality Assurance: Context engineering provides the structured approach necessary for code review, compliance, and audit trails that pure vibe coding lacks.

Vibe Coding with Context Engineering

When generating complete applications with Claude or any other advanced code model, the difference between success and frustration often hinges on effective context engineering. Unlike simple prompts that ask for isolated code snippets/files, building full applications requires systematic context design that guides Claude through complex, multi-layered development tasks.

The Problem: Inconsistent Application Generation

Traditional approaches to AI-assisted development often lead to the following issues:

• Inconsistent architecture decisions across different parts of the application

• Missing critical components like error handling, security, or scalability considerations

• Lack of cohesive design patterns throughout the codebase

• Poor integration between frontend, backend, and data layers

• Incomplete testing strategies and documentation

• Configuration Management issues

• Not repeatable or Completely reliable

• Requires a lot of validation before it can be taken to production

The Solution: Systematic Context Engineering

I strongly believe context engineering transforms Vibe coding, making it a more structured way of defining the complete requirements and context. It maintains consistency across all components and delivers production-ready applications.

Hands-On Example: Building a Task Management Application

In my blog on context engineering, I had talked about the layers of context that we typically will need to define. We will follow this in our example

PRP for Task Management System

I discussed Product Requirements Prompt (PRP) in detail in my blog. Please go through that before proceeding further.

Here is a sample PRP I created to build a end to ned Task Management application:

# CONTEXT-ENGINEERED PRODUCT REQUIREMENT PROMPT
# Task Management Application
# SYSTEM CONTEXT LAYER
## Role Definition
You are a Senior Full-Stack Developer and Software Architect specializing in modern web development. You have extensive experience building production-ready SaaS applications with enterprise-grade architecture, security, and scalability. You understand business requirements and translate them into robust technical solutions.
## Behavioral Guidelines
- Always prioritize security, performance, and maintainability in every decision
- Follow industry best practices and established design patterns
- Provide complete, working implementations that are production-ready
- Include comprehensive error handling, logging, and monitoring capabilities
- Generate clean, well-documented code that other developers can easily understand and maintain
- Consider edge cases and failure scenarios in all implementations
## Quality Standards
- Code must pass production readiness criteria including security scanning
- All components must be fully functional and properly integrated
- Include comprehensive testing strategy (unit, integration, e2e)
- Provide detailed deployment and operational documentation
- Implement proper monitoring and observability features
# DOMAIN CONTEXT LAYER
## Technology Stack
- **Frontend**: React 18 with TypeScript, Tailwind CSS, React Query for state management
- **Backend**: Node.js with Express.js and TypeScript
- **Database**: PostgreSQL with Prisma ORM
- **Authentication**: JWT tokens with refresh token rotation
- **Real-time**: Socket.io for live updates
- **File Storage**: AWS S3 or local storage with multer
- **Deployment**: Docker containers, ready for cloud deployment
- **Testing**: Jest for unit tests, Cypress for E2E tests
## Architecture Patterns
- Clean Architecture with clear separation of concerns
- RESTful API design with proper HTTP status codes and error handling
- Repository pattern for data access abstraction
- Service layer for business logic
- Middleware pattern for cross-cutting concerns (auth, logging, validation)
- Event-driven architecture for real-time features
## Industry Standards
- **Security**: OWASP Top 10 compliance, input validation, SQL injection prevention, XSS protection
- **Accessibility**: WCAG 2.1 AA compliance for all UI components
- **Performance**: Core Web Vitals optimization, lazy loading, caching strategies
- **Code Quality**: ESLint, Prettier, SonarQube rules adherence
- **API Design**: OpenAPI 3.0 specification compliance
# TASK CONTEXT LAYER
## Application Overview
**Name**: TaskFlow Pro
**Type**: Full-Stack Web Application
**Purpose**: Professional task management system for teams and individuals to organize, track, and collaborate on projects with real-time updates and comprehensive reporting.
## Functional Requirements
### Core Features
1. **User Management**
   - User registration and authentication
   - Profile management with avatars
   - Role-based access control (Admin, Manager, Member)
   - Team invitation and management
2. **Project Management**
   - Create, edit, delete projects
   - Project templates for common workflows
   - Project status tracking (Planning, Active, On Hold, Completed)
   - Project-level permissions and team assignments
3. **Task Management**
   - Create, edit, delete, and assign tasks
   - Task priorities (Low, Medium, High, Critical)
   - Task statuses (Todo, In Progress, Review, Done)
   - Due dates and reminders
   - Task dependencies and subtasks
   - File attachments and comments
   - Time tracking and estimation
4. **Collaboration Features**
   - Real-time task updates across all connected users
   - Comment system with @mentions
   - Activity feeds for projects and tasks
   - Notification system (in-app and email)
5. **Dashboard and Reporting**
   - Personal dashboard with task overview
   - Project progress visualization
   - Time tracking reports
   - Team productivity analytics
   - Customizable widgets
6. **Search and Filtering**
   - Global search across projects and tasks
   - Advanced filtering by status, priority, assignee, date
   - Saved search queries
   - Tag-based organization
## Non-Functional Requirements
- **Performance**: Page load times under 2 seconds, API responses under 500ms
- **Security**: End-to-end encryption for sensitive data, secure session management
- **Scalability**: Support for 10,000+ concurrent users, horizontal scaling capability
- **Availability**: 99.9% uptime, graceful degradation during high load
- **Usability**: Intuitive UI/UX with minimal learning curve, mobile-responsive design
## Technical Constraints
- Must work on modern browsers (Chrome 90+, Firefox 88+, Safari 14+)
- Database size should be optimized for cost-effective scaling
- API rate limiting to prevent abuse
- Offline capability for basic task viewing and creation
# INTERACTION CONTEXT LAYER
## Development Phases
1. **Architecture Design**: Database schema, API design, component architecture
2. **Backend Foundation**: Authentication, database models, core API endpoints
3. **Frontend Foundation**: React components, routing, state management setup
4. **Core Features**: Task and project management functionality
5. **Real-time Features**: Socket.io integration and live updates
6. **Advanced Features**: Search, filtering, reporting, file uploads
7. **Testing**: Comprehensive test suite implementation
8. **Documentation**: API docs, user guides, developer documentation
9. **Production Setup**: Docker configuration, environment setup, deployment guide
## Communication Style
- Explain architectural decisions and trade-offs clearly
- Provide detailed code comments for complex business logic
- Include multiple implementation options when applicable
- Proactively address potential scalability and security concerns
## Error Handling Strategy
- Implement global error handling middleware
- Provide user-friendly error messages
- Include proper logging for debugging
- Graceful degradation for non-critical features
# RESPONSE CONTEXT LAYER
## Output Structure
Please deliver the complete TaskFlow Pro application with the following structure:
### 1. Project Architecture Overview
- System architecture diagram (text-based)
- Database schema design
- API endpoint structure
- Component hierarchy
### 2. Backend Implementation
- Complete Express.js server with TypeScript
- Prisma schema and migrations
- Authentication middleware
- All API routes with proper validation
- Socket.io integration for real-time features
- Error handling and logging
- File upload handling
### 3. Frontend Implementation
- Complete React application with TypeScript
- All components with proper prop types
- React Query setup for API calls
- Socket.io client integration
- Responsive design with Tailwind CSS
- Form validation and error handling
### 4. Database Setup
- Prisma schema file
- Migration files
- Seed data for testing
- Database indexing strategy
### 5. Configuration and Environment
- Environment variable documentation
- Docker configuration files
- Package.json files with all dependencies
- TypeScript configuration
### 6. Testing Implementation
- Jest unit tests for critical functions
- API integration tests
- Frontend component tests
- Test data fixtures
### 7. Documentation
- Complete README with setup instructions
- API documentation with examples
- User guide with screenshots (described)
- Deployment guide
### 8. Production Considerations
- Security best practices implementation
- Performance optimization techniques
- Monitoring and logging setup
- Backup and recovery procedures
## Code Organization Requirements
- Use absolute imports with path mapping
- Implement consistent file naming conventions
- Follow React and Node.js best practices
- Include comprehensive TypeScript typing
- Proper error boundaries and fallback UI
- Responsive design patterns
## Specific Implementation Notes
- Implement proper pagination for all list views
- Use optimistic updates for better UX
- Include loading states and skeleton screens
- Implement proper form validation with helpful error messages
- Use debouncing for search functionality
- Include proper image optimization for avatars and attachments
---
# EXECUTION REQUEST
Please generate the complete TaskFlow Pro application following all the context layers defined above. Ensure every component is production-ready, fully documented, and follows the specified architectural patterns and quality standards.
Start with the project architecture overview and then provide all the implementation files in a logical order that would allow a developer to set up and run the application successfully.

as you can see, this is very comprehensive; I am covering all the technical, functional, and non-functional requirements. This document serves as the baseline for generating the code. Once I generate the baseline, I might tweak a few things to make sure everything works. It also gives me very strong “configuration management” and avoids any configuration drifts (that we typically do a lot when we use AI-assisted coding techniques, with random prompts and tweaks).

Let's now create a working implementation that demonstrates how this context engineering approach produces a complete, production-ready application. I have stored the PRP in a file named prp.md, and I am using Void IDE along with the Claude Sonnet model for this example. Following is the prompt I used to start the code generation.

generate code based on the Product Requirements Prompt prp.md

You can see the video recording of the screencast. I did not record the whole generation, as it took around 20 minutes, and with very minor changes, I was able to run the complete app

There you go… I have encountered many articles claiming that context engineering is detrimental to vibe coding, whereas I strongly believe that it actually enhances vibe coding by allowing agents to generate the entire application without modifying the code.

Hope this is helpful..please leave your feedback and experiences…