Spec-Driven Development with AI: Why Better Specifications Matter
Spec-driven development is getting more attention as AI becomes more deeply involved in software engineering. Instead of asking AI to infer requirements from a conversation, teams define what they are building, how it should behave, and what constraints apply before handing more of the implementation work to AI.
The idea itself isn’t new. Software teams have always worked with requirements and specifications. What’s changing is how important those specifications become when the person reading them isn’t always a person. Let’s review what spec-driven development in AI is and how to apply it in practice.
Spec-driven development, or SDD, is an approach in which a structured specification guides software development from requirements through implementation and validation. The specification describes the expected behavior of the software, important constraints, and the criteria that determine whether the implementation is correct.
That definition can make SDD sound suspiciously similar to traditional requirements documentation. However, there’s a difference: SDD is less about inventing a new type of document and more about the role that document plays in development.
Traditional specifications often sit upstream from implementation. A product manager or business analyst writes requirements, developers interpret them, and the implementation gradually becomes the most accurate representation of how the system actually works. Documentation may be updated later, but in practice it can drift away from the product.
In a spec-driven development workflow, the specification remains much closer to the implementation process. It can be used to plan work, provide context to AI coding tools, break requirements into tasks, generate code and tests, and finally check whether what was built actually matches what was requested. AI makes that distinction more important.
Different people mean different things when they talk about spec-driven development. The approach can vary depending on how specifications are used and maintained throughout the development process. In general, we can distinguish three implementation levels: Spec-first, Spec-anchored, and Spec-as-source.
1. Spec-first
The team creates a specification before implementation and uses it to guide AI in generating code and tests. Once the feature is complete, the specification may no longer be maintained. For example, developers document the requirements for an approval workflow before asking AI to implement it.
2. Spec-anchored
The specification remains an actively maintained reference throughout the software's lifecycle. When requirements change, both the specification and implementation are updated to stay aligned. For example, if the approval workflow requires an additional approval level, the team updates the existing specification alongside the code.
3. Spec-as-source
The specification becomes the primary artifact that developers maintain. Instead of editing generated code directly, they update the specification and use AI to regenerate the implementation. For example, adding a new approval rule involves changing the specification, which AI then uses to update the code.
Which approach works best?
As spec-driven development continues to evolve, teams are experimenting with different approaches to integrating specifications into AI-driven workflows. At Univisia, we follow the Spec-anchored approach within our UNITED framework, keeping specifications aligned with implementation throughout the software development lifecycle. However, this doesn't mean other approaches are less valid. Each offers a different way of working with specifications and may be useful in different contexts.
Prompt engineering has become an expected part of working with AI. Developers quickly learn that a vague request produces a vague result, while more context and clearer instructions usually improve the output.
The problem is that better prompting and better requirements solve different problems.
A prompt tells AI what you want it to do now. A specification establishes what the software is supposed to do throughout the task. For a small, self-contained change, those things may effectively be the same. For larger work, they begin to separate.
Suppose a team asks an AI coding assistant to add an approval workflow to an existing application. The initial prompt might explain who submits a request and who approves it. The AI creates the implementation, but then questions appear. Can the person who submitted the request edit it while approval is pending? What happens after a rejection? Can the same person submit and approve under certain circumstances? Does a change to an approved request restart the process?
The developer can keep answering these questions through additional prompts. Eventually, the conversation may contain enough information for AI to produce the right result.
This works until the conversation becomes long, another developer needs to understand the decisions, a different AI agent works on the next task, or the feature needs to be changed six months later. Important requirements are now mixed with implementation discussions, corrections, and earlier suggestions that may no longer be valid.
A specification gives those decisions somewhere more stable to live.
This doesn’t make prompts unimportant. Developers still need to tell AI what they want it to do, provide relevant context, and steer the work when necessary. The distinction is that a prompt should not have to carry the entire history and intent of the feature every time AI performs a task.
For AI-assisted software development, better prompting improves an interaction. Better specifications improve the foundation those interactions depend on.
The practical workflow behind SDD is fairly straightforward. Requirements are defined and clarified before implementation begins. The team then determines the technical approach, breaks the work into manageable tasks, implements the solution, and validates the result against the original requirements.
AI can participate throughout that process by identifying ambiguity in an initial requirement, suggesting questions that still need answers, or turning rough notes into a more structured specification. Once the requirements are clear, it can help explore the existing codebase and prepare an implementation plan. During development, the same specification provides context for generating code and tests. When the work is complete, the expected behavior and acceptance criteria provide a reference for reviewing the result.
This becomes particularly useful when AI performs larger pieces of work. A developer asking an assistant to explain a function can easily judge whether the answer is useful. If an agent is asked to implement an entire feature across several parts of a product, there are many more opportunities for small assumptions to push the result in the wrong direction.
This is also where spec-driven development differs from vibe coding. In vibe coding, developers rely on conversational prompts to generate and adjust code, often without clearly defining requirements or technical constraints upfront. SDD introduces a more structured approach: teams clarify requirements, establish boundaries, and validate AI-generated code against agreed specifications. This makes it easier to maintain consistency and control as development becomes more complex.
Specifications give the agent boundaries within which it can work more independently. That doesn’t mean every decision has to be made upfront. Software development is rarely that predictable. Implementation may reveal a limitation nobody considered, user feedback may change a requirement, or an apparently sensible technical approach may turn out to be unnecessarily complicated.
When that happens, the specification should change too. This is an important distinction between modern spec-driven development and the kind of heavyweight upfront documentation many developers understandably want to avoid.
AI can help maintain that description, but people still need to decide when the description itself is wrong.
Spec-driven development can be valuable for both greenfield projects, where software is built from scratch, and brownfield projects, where teams maintain or modernize existing systems. Its value generally increases with project complexity, expected lifespan, and the cost of getting requirements wrong.
For new projects, SDD helps establish clear requirements, architectural constraints, and expected behavior from the beginning. For existing systems, it helps document business rules, integrations, and technical dependencies that AI tools need to understand before making changes.
Specifications become particularly useful for long-term projects involving multiple developers or AI agents. A shared specification helps maintain consistency across implementation, testing, and future updates, reducing reliance on knowledge held by individual team members.
However, SDD isn't always worth the effort. For throwaway projects, quick prototypes, or short-lived experiments, creating and maintaining detailed specifications may introduce more overhead than value.
The right level of detail should reflect the project's complexity and long-term needs. The goal isn't to document everything upfront, but to establish a reliable foundation that can evolve alongside the software .
One of the risks of a new development approach becoming popular is that teams adopt the visible parts of it without addressing the underlying problem. With SDD, that can mean producing more documents without producing more clarity. A few problems are particularly easy to run into:
The early phase of AI-assisted development has understandably focused on prompts. Developers learned how to describe tasks better, provide the right context, and steer coding assistants toward more useful output.
As AI takes responsibility for larger pieces of development work, that conversational model starts to show its limits. This is why spec-driven development fits naturally into a broader move toward AI-native software development. AI can take on more of the execution, but only when the intent behind that execution is clear enough to guide and evaluate it.
At Univisia, the same principle sits behind our UNITED approach to the software development lifecycle. AI can support work across requirements, implementation, testing, and other stages of delivery, while senior engineers remain responsible for architecture, quality, security, and the final outcome.
The point isn’t to remove people from software development. It’s to make the division of work more useful. AI is increasingly good at execution, exploration, and producing first versions quickly. People still need to decide what matters, resolve ambiguity, challenge assumptions, and determine whether the result is actually right.
If your team is already experimenting with AI development tools and wants to understand where AI can realistically fit into your software development process, book a free AI assessment session with Univisia. We’ll review your current workflow, identify practical opportunities for AI adoption, and help determine what needs to be in place before you scale it.
Prompt-driven development relies primarily on conversational instructions given to an AI tool as development progresses. Spec-driven development establishes the requirements and constraints in a more persistent form that can guide planning, implementation, and validation. Prompts still play a role in SDD, but they do not have to carry the entire context of the feature.
No. Spec-driven development is an approach rather than a requirement to use a particular platform. Tools such as GitHub Spec Kit and Kiro provide workflows designed around specifications, but teams can apply the same principles with their existing development tools and documentation processes.
Yes. SDD can be particularly useful for existing products because new development has to respect established business rules, architecture, integrations, and technical constraints. The specification can capture the context relevant to a change without requiring the team to redesign its entire development process.