Beyond the Prompt

As a technology leader, I’ve learned that the only constant is the blistering pace of change. What was revolutionary yesterday is foundational today and legacy tomorrow. Nowhere is this more apparent than in the world of Generative AI. Just a few years ago, we were marvelling at the ability to generate code from a simple natural language prompt. Today, that feels like the equivalent of a dial-up modem.

We are in the midst of a profound shift in how we create software and drive business value—a rapid evolution from simple prompting to a more intuitive “Vibe Coding,” and now, to the cusp of a truly transformative paradigm: Spec-Based Coding. This isn’t just an incremental improvement; it’s a fundamental change in the relationship between human intent and machine execution.

Let’s chart this journey to understand where we are and, more importantly, where we’re headed.

Phase 1: The age of the Prompt (The Spark)

The era of Generative AI in coding began with the prompt. Tools like GitHub Copilot and early versions of ChatGPT introduced a magical experience: you describe a function in a comment, and the code appears.

// Write a Python function that takes a URL and returns the top 5 most common words on the page

This was revolutionary for several reasons: it democratized basic code generation, eliminated mountains of boilerplates, and served as an incredible learning tool. However, we quickly hit its ceiling. The quality of the output was entirely dependent on the “art” of prompt engineering. Ambiguity in the prompt led to buggy or irrelevant code. It lacked context beyond the immediate file, making it unsuitable for complex, system-wide tasks. It was a fantastic co-pilot for the line-by-line journey, but it couldn’t read the map.

Phase 2: The rise of “Vibe Coding” (The Conversation)

As models became more conversational and context-aware, developers naturally evolved their interaction style—what I refer to as “Vibe Coding.” This isn’t about a single, perfect prompt. It’s a dialogue. It’s the back-and-forth you have with an AI assistant like GPT-4o or Claude 3. It looks something like this:

1. Developer: “Scaffold a basic REST API using Flask with endpoints for users.”
2. AI: Generates initial code.
3. Developer: “Okay, good start. Now add SQLAlchemy models for a User and Order. A user can have many orders.”
4. AI: Refactors and adds models.
5. Developer: “Refactor the database logic into a separate services layer and add basic error handling for the /users/<id> endpoint.”

This conversational flow feels more like pair programming. The developer guides the AI based on an intuitive “vibe” or a mental blueprint. It’s more powerful than simple prompting because it allows for iterative refinement and exploration. However, it’s still fundamentally limited. The process relies heavily on the developer’s constant vigilance to guide, correct, and integrate. It’s effective for features, but not for entire systems, and the “vibe” is ephemeral—hard to reproduce, document, or scale.

Phase 3: The dawn of Spec-Based Coding (The Blueprint)

This brings us to the current phase, a direction that leaders at OpenAI and other leading labs are actively shaping. Spec-Based Coding moves beyond conversation to formal, machine-readable specifications.

Instead of telling the AI how to do something step-by-step, we provide it with a rich, structured definition of what we want to build. The “spec” is not a 100-page Word document; it’s a collection of precise, interconnected artifacts that an AI agent can understand and act upon.

What does a modern “spec” look like?

• API Contracts: An OpenAPI (Swagger) yaml file defining every endpoint, request/response body, and status code.
• Data Schemas: SQL DDL, JSON Schema, or Protobuf definitions that describe the exact structure of the data.
• Business Logic: High-level requirements written in a structured format like Gherkin (Given-When-Then).
• Architectural constraints: Directives like “Deploy as serverless functions on AWS Lambda,” “All services must communicate via gRPC,” or “Adhere to hexagonal architecture principles.”
• UI/UX definitions: A Figma design file or a set of wireframes that the AI can visually parse.
• Acceptance criteria: A list of tests that the final code must pass.

The AI’s role transforms from a text generator to an autonomous engineering agent. It ingests the entire spec, formulates a plan, writes the code for multiple components, creates the necessary tests, and even attempts to debug and self-correct based on test failures—all before a human reviews the final pull request. We’re already seeing the first generation of this with tools like Cognition Labs’ Devin and the advanced agentic capabilities being previewed by major AI labs.

Recent discussions and demonstrations from OpenAI, particularly around their GPT-4o model’s multi-modal reasoning and the evolution of their Assistants API, underscore this trajectory. They are building models that don’t just process text but can perceive, reason, and use tools. A model that can see a UI mock-up on a screen and write the code for it is acting on a visual spec. This is the foundation upon which spec-based development will be built.

The impact: A new Operating Model for Tech and Business

The shift to spec-based coding is not just for engineers. It will redefine productivity across the organization. We can think of the productivity function as:

Productivity=f(Specquality​,Modelcapability​,Toolingintegration​)

For Engineers: The focus shifts upwards. Less time is spent on writing boilerplate, wrestling with syntax, or implementing standard patterns. More time is dedicated to what truly matters: robust architecture, high-quality specification design, complex problem-solving, and the critical review of AI-generated systems. We are moving from code writers to system architects and AI orchestrators.

For Business Users: This is the game-changer. A product manager can write business logic in Gherkin, which becomes a direct input for the AI agent. The gap between business requirements and technical implementation shrinks dramatically, reducing the “lost in translation” errors that plague so many projects. The “citizen developer” becomes the “citizen specifier.”

For the Organization: The benefits are exponential. We’re not talking about a 10% productivity boost. We’re talking about a potential order-of-magnitude reduction in the time-to-market for new products and features, with higher consistency and built-in quality assurance.

The Roadmap ahead

We are not entirely there yet. To make spec-based coding a widespread reality, the ecosystem needs to mature in three key areas:

1. Smarter models: We need models with even more robust long-context understanding, sophisticated planning capabilities, and a true capacity for self-correction. They must be able to reason across dozens of files and complex dependencies.
2. Integrated tooling: Our IDEs must evolve. I envision IDEs becoming “Integrated Design Environments” where writing a spec is a first-class citizen. Version control systems will need tools for reviewing AI-generated pull requests that might touch hundreds of files.
3. Evolving skills: Our teams need to be upskilled. The most valuable engineer will be the one who can write a clear, unambiguous, and comprehensive spec that unlocks the full potential of an AI agent.

The journey from prompt to spec is more than just an evolution of technology. It’s an evolution of collaboration. As technologists, is our focus no longer just on adopting the next AI tool, but on architecting the human-AI organization of the future. Preparing our people, processes, and platforms for the age of spec-based development is the most critical and exciting challenge on our roadmap. The future isn’t about writing code; it’s about specifying success.