A software specification, or spec, is a detailed document that outlines the requirements, functionality, and behavior of a software product. For decades, specs have been the blueprint for human developers. But in the age of AI, the audience has expanded. A good spec must now be clear and structured enough for both human engineers and AI development tools to understand and execute.

The core purpose of a spec remains the same: to create a shared understanding of what needs to be built. However, the rise of AI assistants, code generators, and automated testing frameworks means the spec is no longer just a guide—it’s becoming a direct input for machine processes.

An effective spec is composed of several key elements that work together to provide a comprehensive and unambiguous guide for development. When written for a hybrid human-AI audience, each component takes on a new level of importance.

Machines don’t infer intent. A spec for an AI must be ruthlessly literal and avoid ambiguity.

• Use precise language: Avoid subjective terms like “fast,” “user-friendly,” or “robust.” Instead, quantify these qualities. For example, “The API response time must be under 200ms for 99% of requests.”
• Define terminology: Include a glossary for any domain-specific terms. This ensures everyone (and every AI) is working from the same dictionary.
• Adopt formal notations: For complex logic, consider using formal notations like Gherkin (Given-When-Then) which are designed to be both human-readable and machine-executable.

These practices reduce the risk of misinterpretation by both junior developers and AI code generators.

A well-structured spec is scannable for humans and parsable for machines. Using a consistent hierarchy of headings, user stories, and feature descriptions allows readers—human or AI—to quickly find relevant information and understand the relationships between different parts of the system.

A typical structure might include:

1. Overview: A brief summary of the feature and its goals.
2. User Stories: Describing the feature from an end-user’s perspective.
3. Functional Requirements: A detailed breakdown of what the system must do.
4. Non-Functional Requirements: Performance, security, and scalability criteria.
5. Acceptance Criteria: The conditions that must be met for the feature to be considered complete.

Edge cases are where many projects fail and where AI can struggle without explicit guidance. A good spec anticipates and describes how the system should behave in unusual situations.

Consider a simple file upload feature. The “happy path” is a user uploading a valid file. But what about the edge cases?

• The file is larger than the maximum allowed size.
• The file type is not supported.
• The user’s internet connection drops mid-upload.
• The user has insufficient permissions to upload.

Listing these scenarios explicitly ensures that the resulting code is resilient and that automated testing tools know what to validate.

Acceptance criteria are the “definition of done.” They are a checklist of conditions that must be satisfied for the feature to be accepted. For AI tools, these criteria are not just a checklist; they are direct inputs for generating tests.

Well-defined acceptance criteria are:

• Binary: The condition is either met or it is not.
• Testable: It’s possible to write a test that can verify the outcome.
• Focused: Each criterion tests one specific aspect of the feature.

This level of precision allows an AI-powered QA tool to automatically generate unit tests, integration tests, and end-to-end tests that accurately reflect the feature’s requirements.

In a traditional workflow, a developer could ask a product manager for clarification on an ambiguous requirement. While this is still possible, an AI tool will simply make its best guess, which can lead to flawed code that is costly to fix.

A clear, machine-readable spec acts as a “single source of truth” that aligns human and AI efforts. It reduces rework, minimizes ambiguity, and enables a higher degree of automation throughout the development lifecycle, from coding and testing to documentation and deployment.

Moving to an AI-augmented development process introduces new challenges. A common misconception is that AI can “figure out” a vague spec. In reality, the quality of the AI’s output is directly proportional to the quality of its input. “Garbage in, garbage out” still applies.

Another challenge is maintaining the spec. As the product evolves, the spec must be updated. Treating your specs like code—storing them in version control and requiring reviews for changes—is a best practice that ensures they remain an accurate representation of the product.

Building modern software requires clear definitions for who can do what. Kinde provides a structured and machine-readable way to specify critical aspects of your application like user permissions and feature access, effectively acting as a spec for your authorization and release logic.

For example, when you define permissions in Kinde, you create a clear, unambiguous rule (e.g., create:invoice) that both your human developers and your application code can understand. This eliminates ambiguity about what a user is allowed to do.

Similarly, Kinde’s feature flags allow you to specify the exact conditions under which a feature should be available. You can define rules based on user attributes, organization, or environment. This structured approach ensures that your feature release strategy is clearly defined and consistently enforced, serving as a dynamic, living specification for your product’s behavior.

• Define user permissions
• About feature flags