Software development has always been a dance between structure and creativity, between the systematic and the intuitive. We've spent decades perfecting methodologies that emphasise planning, documentation, and predictable outcomes. But as AI large language models become increasingly sophisticated, a fundamentally different approach has emerged, one where people (typically not developers) can describe their vision rather than implement it directly. Vibe coding represents the shift from having to interact with code directly to collaboratively describing and trusting. It's the process of articulating outcomes to an AI agent that takes responsibility for writing the actual code, while the human maintains creative direction, validates results, and guides the overall vision. The 'vibe' comes from feeling your way through the development process, describing what you want rather than building it yourself. A conversational, intuitive process that leverages AI's implementation capabilities while preserving human creativity and judgment.
But in reality, describing a product and trusting your AI to be your technical partner has proven to be flawed. The skills and expertise gained through years of battle scars that any seasoned developer carries cannot yet be assumed. The horror stories of security breaches and risks to personal data are fast becoming a reality and are no longer just theoretical fears. Context engineering emerged as the disciplined evolution of vibe coding. Where vibe coding relied on intuition and hope, context engineering provides structure, repeatability, and control. It's the difference between asking an AI to 'build something cool' and providing it with comprehensive specifications, constraints, and quality standards. This shift acknowledges that while AI can be an incredibly powerful implementation partner, it requires careful orchestration to produce reliable, secure, and maintainable results. Context engineering addresses vibe coding's core weaknesses through systematic approaches. Rather than hoping for good outcomes, it ensures them through deliberate design choices. Scope definition provides clear boundaries that prevent scope creep and context pollution. When you specify that development needs to follow a test-driven development approach, you're mandating a framework that guides every implementation decision.
Tool Selection becomes a deliberate curation process. Using tooling like MCP servers to access the latest documentation, software design patterns, security approaches and findings, tools like Context7 and Tiny Machines amongst a plethora of others have emerged recently. But critically, being able to specify which ones you want to use and when allows you to reduce context pollution by providing focused, relevant information at the right time.
Instead of just using AI tools to help you write code faster, it's a shift towards descriptive programming, where the developer's role evolves from direct implementer to conductor, orchestrating outcomes through clear communication of specifications, requirements, and practices, rather than manual coding. This is a different type of problem solving, and probably appeals to a slightly different mindset. This shift isn't one solely reserved for AI development approaches; programming languages continue to evolve, from machine code to higher-level constructs, and to more modern interpreted languages, allowing developers to focus on the business problems rather than the technical specifics, all working towards abstracting complexity away from developers to increase efficiency. Viewed through this lens, a language-agnostic approach based on technical requirements and design doesn't feel too far in the future.
Key Principles of Context Engineering
The shift from vibe coding to context engineering is about adopting systematic principles that ensure consistent, higher-quality outcomes:
Context Boundaries - Define what's in scope and what's not. Clear boundaries prevent the AI from making assumptions outside your project's requirements and constraints.
Pattern Libraries - Establish reusable architectural patterns that serve as templates for consistent implementation. Instead of reinventing solutions, leverage proven patterns that match your technical approach and business requirements.
Progressive Disclosure - Provide context incrementally as needed. Not every interaction with AI needs the full system architecture, surface the right context for the task at the right time.
Validation Loops - Build continuous verification against requirements into the workflow. Context engineering is an active process of guidance and validation.
Tool Integration - Create seamless workflows between different context sources. The documentation, patterns, security standards, and business requirements should work together as a cohesive system rather than isolated references.
These principles help transform AI development from an unpredictable exercise into a structured engineering approach.
Although this represents a distinct paradigm shift from traditional development, the act of piecing together the right design, approach, and tools to create a viable outcome is one that only experience can bring to the table. In our projects, these "requirements" need to be defined upfront and early. AI code generation is far better with new/greenfield projects than existing code bases, and retrospectively changing architectural/technical approaches is challenging enough for the most experienced developers, let alone AI tools.
The future of development isn't about choosing between human creativity and AI capability, it's about using both of them effectively. Context engineering provides the framework for this path, ensuring that as we delegate more implementation to AI, we maintain control over quality, security, and vision.
Considering two creative relationships: When you commission an artist to paint your family portrait, you provide the vision in the form of: the subjects, the colours, the mood, and the composition; but the artist wields the brush and claims the artistry. Conversely, when a conductor leads an orchestra, the musicians possess technical skill, yet the conductor is credited with bringing the music to life, shaping raw talent into emotional expression. Context engineering sits somewhere between these two. Like commissioning art, you articulate the vision while the AI handles the technical implementation. But like conducting, you're actively guiding the process, making real-time decisions, and shaping the final outcome through continuous direction. The question isn't whether we'll be the artist or the conductor, but recognising that we are engaging in a fundamentally new creative partnership where vision, direction, and technical execution blend in ways we're still learning to navigate.