In software development, there’s a natural tension between planning and action. The rise of AI has amplified this, creating two distinct paths: spec-first, the methodical architecting of a solution, and code-first, the rapid, exploratory path of discovery. Understanding these approaches helps you choose the right one for your project.

The spec-first approach is the traditional blueprint model. It involves defining detailed requirements, user stories, data contracts, and success metrics before writing a single line of production code. It’s a deliberate process focused on minimizing ambiguity and risk by ensuring everyone understands what needs to be built and why. Think of it as charting a detailed map before starting a journey.

The code-first approach, often called exploratory coding, flips this model. It prioritizes experimentation and rapid prototyping, especially when the final outcome is uncertain. Developers start with a hypothesis, a dataset, or a new AI model and begin coding to discover what’s possible. The specification emerges from the lessons learned during this discovery process. This is like exploring a new land and drawing the map as you go.

Artificial intelligence introduces unique variables like model behavior, data quality, and probabilistic outcomes, making the choice of development methodology even more critical.

A spec-first approach in AI begins with a rigorous definition of the problem and desired outcomes. This is essential for systems where predictability and reliability are non-negotiable.

The process typically looks like this:

1. Problem definition: Clearly articulate the business problem and the exact task the AI will perform.
2. Metric selection: Define precise, measurable metrics for success (e.g., “reduce customer support ticket resolution time by 25%” or “achieve 99.5% accuracy in fraud detection”).
3. Data specification: Detail the required input data, its format, and the expected output. This includes defining data sources, cleaning procedures, and labeling standards.
4. Model constraints: Specify constraints such as latency, computational cost, and explainability requirements.

This approach is common when building AI for regulated industries like healthcare or finance, where you must prove the system behaves as expected and meets compliance standards.

A code-first approach thrives in the uncertain, innovative frontiers of AI. It’s the native environment for research and development, where the goal is to explore a new capability rather than build a known product.

This workflow often involves:

1. Formulating a hypothesis: Start with a broad question, like “Can we use large language models (LLMs) to generate useful code summaries?”
2. Rapid experimentation: Use tools like Python notebooks, AI coding assistants, and open-source models to quickly build and test prototypes.
3. Iterative learning: Analyze the results, refine the hypothesis, and iterate on the code. The goal is to learn quickly and identify promising directions.
4. Emergent specification: The discoveries made during experimentation form the basis of a future spec if the prototype proves valuable enough to productize.

This method is ideal for creating novel generative AI applications or exploring untapped potential in a dataset, where the most valuable insights are often found by accident.

Choose the spec-first approach for projects with high stakes, clear requirements, and a low tolerance for ambiguity. It provides the structure and predictability needed to deliver reliable AI-powered features within a larger system.

You should lean toward a spec-first process in these scenarios:

• Mission-critical systems: When failure has significant financial, legal, or safety consequences, such as in medical diagnostic tools or autonomous vehicle controls.
• Integrating with existing systems: When the AI model is a component in a larger application and must adhere to strict API and data contracts.
• Large or distributed teams: When clear specifications are essential for coordinating work across multiple developers, data scientists, and product managers.
• Projects with compliance requirements: When you need a clear audit trail to demonstrate adherence to regulations like GDPR, HIPAA, or financial governance.

A code-first approach is better suited for innovation, research, and situations where the problem or the solution is not yet well-defined. It gives teams the freedom to explore, pivot, and discover value without the constraints of a rigid plan.

Consider a code-first process for these situations:

• Early-stage product development: When you are still searching for product-market fit and need to quickly test different AI-driven value propositions.
• Research and development (R&D): When the goal is to explore the capabilities of a new model or technique to determine its feasibility for future products.
• Creative and generative AI projects: When the objective is to create something novel and the final output is intentionally left open-ended.
• Small, agile teams: When teams are co-located and communication is seamless, allowing for tight feedback loops that make formal documentation less necessary.

For many teams, the most effective strategy isn’t choosing one approach but blending them. This hybrid model combines the freedom of exploration with the discipline of focused execution.

A popular hybrid workflow looks like this:

1. Spec-lite: Begin with a high-level document outlining the core problem, the target user, and the key success metrics. Keep it brief and flexible.
2. Time-boxed exploration: Use a code-first approach to build a proof-of-concept (PoC) or prototype within a fixed timeframe (e.g., a two-week sprint).
3. Evaluate and specify: Analyze the PoC to determine what’s technically feasible and valuable. Use these findings to create a detailed spec for the production version.
4. Build and deliver: With a validated concept and a clear spec, the team can now build the feature with confidence.

This approach allows you to de-risk innovation by grounding your final product specifications in real-world experimentation.

Whether your AI feature was born from a detailed spec or a scrappy prototype, you need a safe and controlled way to get it into the hands of users. This is where managing access and rollout becomes critical. An authentication and user management platform like Kinde provides the tools to bridge the gap between development and delivery.

By using Kinde to manage user identity, you can tie your AI features to specific business rules and user attributes.

For example, you can:

• Use feature flags to release a new AI-powered chatbot to a small group of beta testers. This allows you to gather feedback on a feature that started in a code-first exploration without impacting your entire user base.
• Control feature access with permissions to ensure that only users on a specific subscription plan can use a computationally expensive AI summarization tool. This helps you manage costs and tie advanced features to revenue.
• Assign roles to users to grant access to powerful, admin-level AI tools only to trusted team members, protecting your system from misuse.

These capabilities allow you to ship AI features faster and more safely, regardless of the development methodology you choose.

• Manage feature flags
• Managing roles
• Managing permissions