Why Spec-Driven Development is Making a Comeback with AI

Imagine you could describe exactly what you want your software to do, and moments later, have working code that does it. This isn't science fiction—it's happening right now through a combination of detailed specifications and artificial intelligence.

Think of building software like building a house. You wouldn't tell a construction crew "build me something nice" and expect good results. You'd start with detailed blueprints showing exactly what goes where, how rooms connect, and what materials to use. Software works the same way, except now those blueprints—called specifications—can do something remarkable: they can directly create the finished product.

This is the power of spec-driven development in the age of AI, and it's changing everything about how quickly and reliably software gets built.

What is Spec-Driven Development?

Spec-driven development is simple: you write detailed descriptions of what your software should do before anyone starts building it. These descriptions—called specifications or "specs"—explain the features, how they work, and what users can do with them.

Let's use a concrete example. Say you're building a login feature. A good specification would describe:

• What the login screen looks like (email field, password field, login button)
• What happens when someone clicks the login button
• What happens if they enter the wrong password (show an error message)
• What happens if they enter the right password (take them to their dashboard)
• What if they forget their password? (show a reset link)

Notice what the spec doesn't do: it doesn't explain the technical details of how to code it. That's not its job. The spec describes what you want and why it matters, leaving the how to the people (or AI) doing the building.

How AI Uses Specifications

Here's where it gets interesting. When you combine detailed specifications with AI, the development process looks like this:

1. Write the spec: You describe exactly what you want the software to do
2. AI reads it: The AI assistant reads and understands your requirements
3. AI generates code: The AI writes the actual code that makes it work
4. Review and refine: A developer reviews the code and makes any needed adjustments

Why does this work so well? Because AI is much better at following clear, detailed instructions than making guesses about what you want.

Think of it like giving directions. If you tell someone "go to the store," they have to figure out which store, which route, and what to do when they get there. But if you say "drive to the grocery store on Main Street, park in the west lot, and buy 2 litres of milk," they know exactly what to do. AI works the same way—the clearer your instructions, the better the results.

The Game-Changer: Executable Specifications

Here's the paradigm shift that makes this revolutionary: specifications are becoming executable.

In the past, a specification was like a instruction manual. You'd write down what you wanted, then hand it to developers who would read it and manually write code to build it. The spec guided the work, but humans did all the building.

Now, with AI, the specification itself can directly produce working software. It's the difference between:

• Old way: Architectural blueprint you hand to construction workers who build the house
• New way: A 3D printer file that creates the house automatically

Or think of it like cooking:

• Old way: A recipe that describes a dish to a human chef
• New way: A program that a robot chef executes to cook the dish automatically

This is profound: well-written specifications are now essentially instructions that AI can execute to create software. Documentation has become implementation.

The impact? Dramatic speed increases. You skip the entire step of manually translating written requirements into code. Write the spec, AI builds it. Change the spec, AI updates it. The specification and the software stay in perfect sync because they're basically the same thing.

Why This Makes Development Faster

When you use specifications with AI, three things happen that dramatically speed up development:

1. Less Back-and-Forth

Without a clear spec, developers spend huge amounts of time asking questions: "What should happen here?" "What did you mean by that?" "Should this work this way or that way?"

With a detailed spec, AI knows what to build from the start. No guessing, no waiting for answers. Just build.

2. Fewer Mistakes

Vague requirements lead to mistakes. The AI (or developer) makes assumptions about what you want, builds it, then you say "no, not like that." Now they have to rebuild it.

Clear specifications mean fewer bugs and fewer rewrites. The AI builds it right the first time because it knows exactly what "right" means.

3. Easy to Change

Here's where the "executable" nature really shines. Need to change something? Update the specification, and the AI can regenerate the code instantly. No more "that'll take two weeks to change." Change the spec, change the software.

Compare two approaches:

• Vague idea: "Build me a shopping cart." AI guesses what you mean, builds something, you don't like it, back to square one.
• Detailed spec: "Shopping cart with product image, name, price, quantity selector (1-99), remove button, and running total at bottom." AI builds exactly that, first try.

What Makes a Good Specification?

Not all specs are created equal. Good specifications that AI can execute have these elements:

Clear Description of What It Does

Explain the feature in plain language. "Users can search for products by typing in the search box. Results appear below as they type, showing product name and price."

Examples of How Users Interact

Walk through the actual steps: "User clicks 'Add to Cart' button. Button changes to say 'Added!' for 2 seconds. Cart icon in header updates to show new item count."

What Happens in Different Scenarios

Cover the success case: "If login is successful, take user to dashboard." Cover the error cases: "If password is wrong, show 'Incorrect password' message in red below the password field." Cover edge cases: "If user hasn't verified email, show message asking them to check their inbox."

Important Rules or Constraints

Note anything that must be true: "Passwords must be at least 8 characters." "Product searches must return results in under 2 seconds." "Users can only see their own orders, not other users' orders."

The key is being detailed enough that someone (or some AI) could build exactly what you're describing without having to guess or make assumptions.

A Real-World Example

Let's see how this works with a concrete example: building a checkout flow for an e-commerce site.

The Specification:

"Checkout Page Requirements:

• Display cart items at top: product image (100x100px), name, price, quantity, subtotal
• Show order summary box on right: subtotal, shipping ($9.95 flat rate), GST (calculated at 10%), total
• Collect shipping info: full name, address, suburb, state, postcode - all required fields
• Collect payment info: card number, expiration date, CVV, billing postcode
• Add 'Place Order' button at bottom - disabled until all fields are valid
• When user clicks Place Order: show loading spinner, process payment, show confirmation screen with order number
• If payment fails: show error message 'Payment declined. Please try a different card.' and re-enable form"

What AI Does:

With this specification, an AI coding assistant can generate:

• The page layout with all the visual elements
• The calculation logic for GST and totals
• Form validation for all the fields
• The payment processing flow
• Error handling for failed payments
• The confirmation screen

All of this happens in minutes instead of hours or days. And because the spec is clear about every detail, the generated code does exactly what's needed. This is the "executable" nature in action—the spec directly becomes working software.

Common Questions

How detailed should specifications be?

Detailed enough that AI can execute them without guessing. If you're writing "build a login page," that's too vague. If you're specifying every pixel and code structure, that's too much. Aim for the middle: describe what users see and do, and what happens in response.

Do I need technical knowledge to write specs?

Not necessarily. You need to understand what you want the software to do, not how to code it. That said, understanding basic software concepts helps you write clearer specs.

Does this work for all projects?

It works best for projects where you can define what you want upfront. If you're still figuring out what to build, start with a smaller spec for one feature, build it, learn from it, then spec the next feature.

What if my requirements change?

That's the beauty of executable specifications—you just update the spec and AI regenerates the code. Changes that used to take days now take minutes.

Can specifications really replace coding?

They become a new form of coding. Instead of writing in programming languages, you're writing detailed descriptions that AI translates into code. You're still creating the logic and structure, just in a more human-readable way.

The Bottom Line

Here's what matters: specifications combined with AI create faster, more accurate software development.

The big idea is simple but powerful: specifications have evolved from documentation into executable programs. You're not writing specs that guide development—you're writing specs that are the development.

Who benefits from this?

• Development teams who want to ship faster
• Companies building software products
• Anyone who needs to hire developers and wants predictable results

The fundamental shift is moving from "write specs, then spend weeks coding" to "write specs, AI builds it immediately." The specification becomes the software.

This isn't some distant future technology. It's happening now, and it's becoming standard practice for teams that want to move fast without sacrificing quality.

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How Dev Force One Uses Spec-Driven Development

At Dev Force One, every project starts with detailed specifications that become executable blueprints for development.

Our approach delivers real results:

• Ship MVPs in weeks: Clear specifications let AI generate working code immediately
• High quality: Specifications ensure proper error handling and edge cases from day one
• Easy changes: Update the spec, update the software—no expensive rewrites

We're not just using AI tools. We're combining clear, executable specifications with AI to build better software faster.

Ready to see what spec-driven development can do for your project? Book a call to discuss your idea.

Write the spec. AI builds it. Ship faster.