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AI has clearly changed the way many teams approach software development. In 2026, it is possible to generate components, functions, tests, documentation or complete prototypes at a speed that, until recently, seemed unthinkable. An idea that once required several hours of work can become an initial functional version in a matter of minutes.
But I believe that speed also comes with a trade-off. Generating code faster does not necessarily mean developing better. The real challenge is not merely getting a tool to write lines of code, but building useful, maintainable software that is consistent with the product and aligned with a real objective. When that objective is not well defined, artificial intelligence can produce technically plausible solutions that are poorly suited to the context in which they need to work.
This is where one of the limits of so-called vibe coding appears: moving forward intuitively, trying prompts, accepting responses and correcting as you go, without a sufficiently clear definition of what you want to build. The problem is not using AI to code. The problem is delegating implementation too early, before the scope, rules, edge cases or criteria that will determine whether the solution is correct have been properly thought through.
In this context, Specification-Driven Development proposes a shift in approach. Before asking for code, intention is defined more clearly. Before generating solutions, the problem is clarified. And before speeding up implementation, a specification is created to serve as a shared reference for people, teams and AI tools. Because at a time when coding can be faster than ever, knowing what to build, why, and under what conditions becomes more important than simply producing code.
What Specification-Driven Development really is
Specification-Driven Development, or SDD, starts from a simple idea: before implementing, it is worth clearly defining what you want to build. It is not just about writing a list of requirements, but about creating a specification that helps organise the problem, the scope, the important decisions and the criteria that will make it possible to validate whether the result is correct.
In traditional development, the specification is often separated from the real work. It may appear at the beginning of a project, during a documentation phase, or as a document that soon stops being updated. In today’s context, shaped by AI tools and agents capable of generating code, the specification takes on a more active role: it becomes a working guide. It does not only guide the human team, but also the systems involved in implementation.
For that reason, an SDD specification should not be understood as decorative documentation. Its purpose is not to create an appearance of order or fill a repository with texts that nobody consults. Its value lies in becoming a shared point of reference: what must be built, what is out of scope, what constraints must be respected, what behaviours are acceptable and how it will be checked that the solution works as expected.
This is especially important when working with AI. An isolated prompt can generate a useful response, but it can also leave too many decisions open. A good specification, by contrast, reduces ambiguity. It helps AI not only produce code, but do so within specific boundaries, with a defined intention and clearer criteria for reviewing the result.
The term is still evolving. Martin Fowler describes it, in general terms, as a practice in which a specification is written before generating code with AI, so that the specification acts as a source of truth for both people and the agents involved in the process. What matters is not turning SDD into a rigid methodology, but understanding the change in mindset it proposes: moving from asking for code directly to first defining the framework in which that code needs to make sense.
SDD does not replace technical judgement. It does not remove the need to review architecture, security, performance, maintainability or user experience. Nor does it turn a bad idea into a good product simply because it is documented. What it does do is organise the work better. It forces better questions to be formulated before looking for answers and helps ensure that the speed brought by AI does not become accelerated improvisation.
Why SDD is becoming more important in the age of AI agents
Interest in Specification-Driven Development has not emerged by chance. It is directly related to the evolution of AI tools applied to software development. At first, many uses focused on requesting code snippets, resolving specific questions or generating small functions. But the landscape is changing: AI agents no longer only answer a one-off question; they can analyse a repository, modify several files, propose an architecture, create tests, document changes or carry out chained tasks.
This capability opens up enormous possibilities, but it also increases risk. The more autonomous a tool is, the more important it becomes to properly define the framework in which it should act. An agent can move quickly, but it needs context. It needs to know what problem it is trying to solve, what constraints it must respect, which decisions have already been made, which behaviours it must not break and what criteria will make it possible to assess whether the result is valid.
Without that information, AI can generate solutions that appear correct but are wrong for the project. It can create code that compiles but does not fit the existing architecture. It can solve the most obvious case while overlooking edge cases. It can introduce unnecessary dependencies, duplicate logic, break internal conventions or propose a solution that works in isolation but not within the real system.
This is why SDD is becoming more important. Not because specification is an entirely new concept, but because in an AI-assisted environment it becomes a means of control, alignment and quality. The specification helps reduce ambiguity before it turns into code. It also means that decisions do not depend solely on improvised prompts or subsequent corrections, but on an intention defined from the outset.
Tools such as GitHub Spec Kit are built on precisely this logic: preventing AI-assisted development from starting with vague instructions and instead progressing from requirements, motivations and technical details defined before implementation. The aim is not to slow down AI, but to give it clearer direction. Rather than simply asking it to “build this feature”, the team first defines what that feature needs to solve, for whom, within what limits and under what conditions it can be considered complete.
This change matters because technical debt often does not arise from one major wrong decision, but from an accumulation of small ambiguities. An undefined validation, an implicit business rule, a forgotten edge case or a misunderstood integration may seem like minor details at first. But when AI generates code at great speed, those gaps multiply more quickly too.
SDD helps introduce an intelligent pause before execution. Not a bureaucratic pause, but a pause for clarity. It makes it possible to think before producing, align before automating and validate before assuming that something works. In the age of AI agents, this ability to define work well may be as important as the ability to write code.
What a good specification should include
A good specification does not have to be an endless document. Nor should it become a barrier that delays any progress. Its purpose is much more practical: to make explicit what would otherwise remain scattered across conversations, assumptions, implicit decisions and later corrections.
The first point is to properly define the problem to be solved. Before describing screens, components or functions, it is worth explaining what need exists and why it is worth addressing. Saying “we need a new form” is not the same as saying “we need to reduce drop-offs in the registration process because many users do not complete account creation on mobile”. The second wording already introduces context, intention and a clearer criterion for making decisions.
From there, the specification should set out the functional objective. In other words, what the solution should enable users to do. If we are talking about a registration form, it is not enough to ask for “a form”. We need to define what data it should collect, which fields are mandatory, which validations apply, what happens if the email address already exists, how registration is confirmed, which error messages are shown and how it should behave on different devices.
It is also important to identify the affected users or use cases. The same feature can have different implications for a new user, a returning user, an administrator or an internal team. AI may generate a solution that appears valid for the main case, while leaving secondary scenarios unresolved even though they matter to the business or the product’s real-world experience.
Another key element is defining the scope. A good specification should explain what is included and what is excluded. This part prevents many misunderstandings. For example, a feature may include registration by email and password, but not authentication with Google or LinkedIn. It may include basic validation, but not a full CRM integration. It may cover the mobile version, while leaving certain advanced accessibility adjustments for a later phase. Defining these boundaries does not reduce the project’s ambition; it makes it more manageable.
The specification should also capture technical constraints. This includes the framework to be used, project conventions, permitted dependencies, existing architectural patterns, performance, security or compatibility requirements, and any technical decision that should not be left to chance. In an AI environment, this section is particularly relevant because the model may propose solutions that are correct in abstract terms but poorly suited to the project’s actual stack.
Alongside this, it is worth including business rules and edge cases. Many features fail not because the main flow is poorly designed, but because exceptions were not thought through properly. What happens if a user tries to register with an email address that has already been used? What happens if they lose connection halfway through the process? How should the system respond if an external API is unavailable? What message does the user receive if the password does not meet the requirements? These details are what separate a quick prototype from a solution prepared to work in a real environment.
Finally, a specification should include acceptance and validation criteria. That is, how we will know that the feature is complete and working correctly. This may include manual testing, automated tests, accessibility review, performance checks or security validation. Without these criteria, development remains open to overly subjective interpretations: it seems to work, but nobody has precisely defined what working means.
In this sense, a good specification does not only help AI generate better responses. It also helps the team think better. It forces intuitions to become decisions, wishes to become requirements and expectations to become verifiable criteria. And that clarity is precisely what prevents the speed of AI-assisted development from becoming a succession of quick but fragile solutions.
SDD is not a return to heavyweight development: it is about reducing ambiguity
One of the first objections to Specification-Driven Development is understandable: does it mean returning to writing long documents before being able to code? Is it a way of bringing back heavyweight, slow and inflexible processes? The answer should be clear: no. SDD is not about filling a project with static documentation, but about reducing ambiguity before that ambiguity turns into code.
For that reason, specification should not be confused with bureaucracy. A useful specification is not an endless, closed and immovable document that attempts to anticipate everything from day one. It is a working tool. It can start out brief, grow as the project becomes more complex, be versioned alongside the code and be reviewed when product needs change or new learning emerges.
In that sense, SDD does not have to compete with agile methodologies. It can coexist perfectly well with short iterations, incremental delivery and frequent feedback cycles. In fact, it can strengthen them. A clear specification does not prevent iteration; it helps ensure that each iteration has a more precise direction. It makes it possible to know what is being tested, which hypothesis is being validated and what criteria need to be met before a feature can be considered ready.
The difference lies in the approach. It is not about documenting everything in advance or blocking development until every detail has been settled. It is about documenting enough for people and AI tools to work within a shared framework. In many cases, a good specification can be much lighter than a traditional document: an explanation of the problem, a few acceptance criteria, some technical constraints, relevant edge cases and a clear definition of what is in and out of scope.
This clarity is especially valuable when AI agents are involved. If the human team can already misinterpret a poorly defined task, an automated tool can do so too. The difference is that it can move very quickly in the wrong direction. SDD introduces an earlier layer of shared intention: not to slow down the process, but to prevent unnecessary corrections, rework and implicit decisions that later become difficult to undo.
Understood in this way, specification is not a brake on creativity or speed. It is a way of protecting both. It makes it possible to experiment without losing the framework, accelerate without ceasing to validate, and collaborate with AI without turning every task into an improvised negotiation with a prompt. When development rests on a clear intention, the team can move faster because it spends less time debating what should have been defined beforehand.
This is why SDD should not be seen as a return to the past, but as a necessary adaptation to the present. In an environment where generating code is becoming ever easier, the value does not lie in producing more documentation, but in creating better points of reference. It is not about writing for the sake of writing. It is about sharing an intention that is clear enough for software to be built more consistently, with less ambiguity and less future debt.
From the developer who writes code to the team that designs decisions
The arrival of AI in software development does not eliminate the value of the developer. It shifts it towards areas where human judgement is even more important. If a tool can generate code, propose solutions or modify several files in a short time, the question is no longer only who writes each line, but who defines the problem, who evaluates the solution and who decides whether what has been generated makes sense within the product.
In this new context, technical work does not lose relevance. On the contrary, it requires a broader perspective. The developer must no longer merely implement a task, but formulate it well, anticipate risks, review architectural decisions, spot inconsistencies, protect the maintainability of the system and validate whether a solution fits the project’s real needs. AI can accelerate execution, but it does not replace the responsibility of deciding which direction is worth pursuing.
Here, Specification-Driven Development makes an important contribution: it turns the specification into a space for alignment. It is not merely a technical document for someone to code from afterwards. It is a place where product, design, business and development can clarify what they want to achieve before translating it into code. That earlier conversation can prevent many later problems because it forces decisions that are usually hidden behind overly general phrases to become visible.
For example, a request such as “improve the user registration process” can mean different things to each role. For the business, it may mean increasing conversion. For UX, reducing friction. For development, simplifying validation or improving integration with external systems. For support, reducing recurring errors. Without a clear specification, all those expectations can coexist implicitly until conflicts arise. With SDD, those differences are brought to the table before implementation.
This also helps improve collaboration between technical and non-technical roles. A good specification should not be written only for programmers, nor should it be limited to a superficial business description. It should act as a bridge: clear enough for everyone involved to understand the objective, and precise enough for the technical team to turn it into a robust solution. A significant part of its value lies in that balance.
In digital projects, many technical decisions have product, user experience and business consequences. Choosing how to validate data, when to show an error, what information to store, how to structure a flow or what to automate are not purely internal code decisions. They affect the final experience, operational efficiency, security and the ability to scale. SDD helps ensure that these decisions are not made by accident or delegated to an automatic AI response.
This is why specification-driven development should not be understood as an isolated practice of the technical team. It can become a more mature way of designing digital decisions. The specification enables the team to ask not only “what needs to be coded?”, but also “what problem are we solving?”, “what risks should we avoid?”, “how will we know this works?” and “what impact will this decision have on the product as a whole?”.
At a time shaped by automation, that ability to formulate better decisions may be more valuable than execution speed. Because the future of development will not depend solely on teams able to produce code faster, but on teams able to think better before producing it.
Risks and limitations of Specification-Driven Development
Specification-Driven Development can bring order, clarity and better collaboration with AI tools, but it should not be presented as an automatic solution. A specification does not guarantee a good result on its own. In fact, if it is poorly framed, it can produce the opposite effect: making AI generate bad code faster, with greater confidence and with an appearance of rigour that makes the problem harder to detect.
The first risk lies in confusing a written specification with a correct specification. A document can be clear in form but wrong in substance. It may define the problem incorrectly, overlook an important business rule, oversimplify the context or assume technical constraints that do not fit the real system. In that case, AI is not solving the task better; it is simply executing a flawed instruction quickly.
There is also the opposite risk: specifying too much. If every feature becomes trapped in excessive detail, the team may lose its ability to iterate. A useful specification should guide, not block. It should establish intention, boundaries and validation criteria, but without becoming a contract so rigid that it prevents learning, correction or adaptation as new information emerges. The objective is not to anticipate everything, but to reduce enough ambiguity to move forward with better judgement.
Another important limitation is that AI can still misinterpret the context. Even with a good specification, an agent may not fully understand the project architecture, internal conventions, existing dependencies or technical decisions made in the past. It may invent an API that does not exist, use a pattern that does not fit, duplicate logic that has already been solved or introduce a solution that seems reasonable in abstract terms but does not correspond to the real repository.
Some recent research on SDD and AI agents points in precisely this direction: the challenge is not only writing better specifications, but ensuring that agents are properly connected to the project’s real context. A paper published on arXiv about Spec Kit Agents notes that agents can remain “context-blind” in large, changing repositories, which can lead to invented APIs or architectural violations if there are no mechanisms for connecting and validating against the real environment.
This is why specifications should not live separately from the system. They need to be checked against the existing code, tests, dependencies, team conventions and the product’s actual behaviour. A specification that is not validated can become a source of false confidence. The work appears well defined, but it may be disconnected from what the system allows, needs or already contains.
Human judgement remains essential here. SDD can help organise work, but it does not replace technical review, conversation between roles, product knowledge or the responsibility to make decisions. AI can propose, generate and accelerate. The specification can guide. But someone must check whether the solution is appropriate, whether it respects the architecture, protects the user experience, avoids unnecessary debt and genuinely addresses the problem it was intended to solve.
Ultimately, SDD does not eliminate the risks of AI-assisted development. It makes them more visible and manageable. Its value does not lie in promising perfect results, but in creating a clearer framework for thinking, building, reviewing and correcting. And perhaps that is its greatest strength: it does not replace the team’s judgement, but requires it to appear before the code begins to multiply.
Conclusion
Specification-Driven Development should not be understood as just another methodological trend or as a sophisticated way of writing documentation. Its value lies in something deeper: turning specification into a thinking tool. Before building, it requires formulation. Before automating, it requires clarification. Before asking for code, it requires defining intention.
This idea is especially important at a time when AI can produce software at ever greater speed. When generating code becomes easier, the difficulty moves elsewhere: knowing what is worth building, why, for whom, within what limits and under what criteria we can say it works. The advantage will not lie only with whoever can produce faster, but with whoever can direct that speed better.
SDD proposes precisely that strategic pause. Not a pause to slow development down, but to prevent execution from moving ahead without sufficient direction. A good specification helps distinguish a vague idea from a real need, an apparent solution from an appropriate solution, and a generated feature from a validated feature. In that process, the team does not merely document what it wants to do: it learns to think better about what it is trying to build.
It also changes the relationship with AI. Rather than treating it as a machine from which immediate answers are requested, SDD encourages using it within a clearer framework. AI can help implement, review, propose alternatives or detect inconsistencies, but it needs direction. And that direction does not arise from an isolated clever prompt, but from a shared understanding of the problem, the context and the success criteria.
This is why the new skill is not simply knowing how to ask for code. It is knowing how to define intention. It is turning scattered requirements into understandable decisions. It is translating product, business, design and technology objectives into a specification that makes it possible to build more consistently. It is understanding that the quality of the result depends as much on the tool that generates it as on the clarity with which it is told where to go.
Perhaps the future of development will not depend solely on writing better prompts, but on learning to formulate better specifications. Because when code accelerates, intention becomes more important than ever.
Are we using AI to write code faster or to build software with greater intention?
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