Software development is transforming, and we invite you to reflect on this evolution. In the past, teams built software by carefully planning out features and then writing code based on these technical plans (called “spec development”). AI-assisted development is a new approach that leverages generative AI tools to support writing and testing code.

Many startup founders wonder which method yields the best results, faster, cheaper, and more effectively. Let’s take a closer look at the subject. We’ll examine the differences between spec-driven and vibe coding in a professional yet conversational manner, focusing on speed, cost, creativity, reliability, and market impact. We’ll back up each comparison with data and real-world examples to see which method might “win” or if the future will be a mix of both.

What is development that is based on specifications?

The traditional way to build software is first to write a clear requirements specification, create a system architecture, and design. An experienced system architect typically verifies these artifacts to ensure the system will not have any performance or security issues. When the entire team is not on the same page, what should be built and how it should be built, the work begins.

This is called spec-driven development (SDD). This method, often associated with structured Agile, provides discipline and predictability. Teams invest significant time in planning, architecture, and documentation to have an opportunity to share a “single source” about what to build, reducing ambiguity during coding.

Spec-driven development has been utilized to support numerous enterprise systems, mobile applications, and web platforms. The idea is to inform developers precisely how to implement the system and provide the team with the acceptance criteria to meet.

This structured approach helps build reliable systems with a consistent implementation flow, as changes are carefully managed and nothing is deployed to production without passing a thorough code review and Quality Assurance.

However, nowadays, this bullet-proven method is considered to be slow.

Writing and revising specs, then coding and testing against them, takes time. If requirements change mid-project, a traditional workflow might require time for re-planning and refactoring. Changes are costly.

That is considered the main drawback, especially when building an MVP project in an unverified market.

AI-Assisted Development (Vibe coding)

AI development offers several approaches, including using advanced AI systems (such as GitHub Copilot, OpenAI’s Codex, and Amazon CodeWhisperer) to generate or assist with code, testing, and other development tasks. Developers use prompt-based AI assistants with natural language processing or high-level instructions and receive code suggestions or even entire functions in return.

Teams use artificial intelligence to support the software lifecycle at every stage:

1. Discovery. During the discovery phase, AI can review user stories, user journeys, and competitors with similar products to identify requirements.
2. While coding, tools like Copilot or Replit’s Ghostwriter can finish your code for you or make new modules based on a prompt.
3. AI testing tools can automatically find bugs or make test cases. This method is often more iterative, with developers starting to code with AI suggestions and then improving them through trial and error.

However, even AI coding agents may need clear instructions, as things can become “messy” without them. The AI may create something useful, but it may not be what the user wanted, especially if the instructions are unclear. This is exactly where we still need to carefully think before we start programming, just as with spec-driven development.

How Successful Teams Navigate Modern Software Development?

Beyond project metrics, the rise of AI development is transforming the software industry and the way teams operate. For businesses and startups, being faster and more efficient in development can translate to significant market advantages.

Speed and Productivity

Speed is often cited as the most significant advantage of AI-assisted development. AI can write code much faster than a human typing. In one experiment, developers using GitHub Copilot (an AI pair programmer) completed a coding task 55% faster than those coding manually. Over 90% of users in a GitHub survey said Copilot helps them complete tasks more quickly. (Source: GitHubBlog).

However, the reality of speed is more nuanced. A recent study shocked many by finding that experienced open-source developers took 19% longer to complete tasks when using AI tools than by doing those tasks without AI help (Source: Metr).

It turns out that AI can create a false sense of speed; it writes code quickly upfront, but that initial velocity may be offset by extra time spent debugging, reviewing, and adjusting the AI’s output. Developers noted that AI sometimes generates code that “looks right” at first glance but contains subtle bugs, requiring several hours of debugging to fix.

Why does AI sometimes slow things down? Common reasons include rewriting AI-generated code that doesn’t meet the spec, time spent crafting and refining prompts, and verifying that the code is correct. By contrast, traditional spec-driven development might be slower at the coding step, but because the spec was clear, there may be fewer surprise rework cycles later.

Cost Efficiency

Cost is closely tied to speed and productivity. At first glance, traditional spec development doesn’t require subscription fees for Copilot or cloud AI APIs, and teams can use free open-source tools and pay only for developer salaries. However, the slower pace of spec-based projects incurs costs in the form of extra labor hours, added functionality, and the elimination of technical debt from rushed fixes.

AI-assisted development also requires upfront investment, such as paying for AI services or training developers to utilize new tools. Industry analyses in 2025 suggest that projects that once took a year can now be finished in half the time with the aid of AI. Still, remember that if you are an SMB or a large business, your project will require scaling, and AI tools and infrastructure are not free, plus significant cloud compute costs for model hosting in development. It can also cost more to store and process large datasets for training or running AI features. Additionally, AI systems are more expensive to maintain because they “drift” as real-world data changes, necessitating regular retraining and tuning. The average cost of retaining an AI system is 30–50% of the cost of building it, while the average cost of maintaining traditional software is only 15–25%. This includes costs for acquiring new data, updating models, and expanding infrastructure to handle AI workloads.

In short, AI development can lower development costs, but those savings can be lost if you don’t account for the price of tools and the ongoing maintenance required to keep AI systems accurate.

Creativity and Innovation

Following a strict spec stifles creativity, while an AI that can generate anything opens creative possibilities. In reality, each approach influences creativity in different ways. Like spec-driven development, forcing teams to think deeply about the problem upfront leads to well-considered designs. Because traditional workflows emphasize meeting predefined requirements, developers have less freedom to improvise once the spec is set. This predictability is good for reliability but can limit the testing of novel ideas midstream.

AI development, on the other hand, shakes things up when it comes to creativity. AI coding assistants essentially act as brainstorming sidekicks for developers. They can offer new ideas and point out alternative approaches to generate unexpected solutions. Plus, the old saying goes, by taking over the routine, AI lets us get back to the fun stuff: higher-level creative tasks.

Also, AI tools come with a significant bonus is rapid prototyping. You can quickly create a rough version of any business idea and start testing it in the market. But if you lean too heavily on AI without a clear direction, you might actually find that it just rehashes generic solutions rather than coming up with something fresh.

In short, AI isn’t going to replace our creative brainstorming in software; instead, those who figure out how to use it to supercharge their creativity will be the ones who come out on top.

Reliability and Quality

Reliability includes code quality, correctness, and maintainability. Spec-driven methods naturally enhance reliability: starting with clear requirements and a design usually results in code that closely matches expectations. Developers who follow a spec are less likely to create unintended features. Additionally, traditional workflows often involve detailed code reviews and human QA testing at each stage, which help identify issues before release.

AI-generated code can be a hit-or-miss approach as it comes to reliability. On one hand, AI can produce syntactically correct, well-structured code (it’s trained on best practices from millions of programs). However, there are significant reliability challenges with AI development. The primary issue is that AI doesn’t truly understand the intent behind code, which may lead it to produce outputs that pass tests but fail to meet the user’s actual needs. AI coding tools tend to “hallucinate” APIs or functionalities that don’t exist or to output incorrect solutions if the prompt is ambiguous. This means thorough testing is absolutely required for AI-written code.

In traditional development, fixing bugs often depends on software developers’ skills in detecting and resolving them. AI can also help by analyzing logs or suggesting ways to resolve issues. However, if the bug is in AI-generated code, the developer may not understand how it works, making it difficult to fix.

In the end, spec development is better because you usually receive what you ask for as a client. AI development, on the other hand, needs a new way to ensure quality, which could involve extensive testing and more refactoring. The trend suggests a combination: use AI to write code, but ensure that specifications, tests, and human supervision are always in place to catch mistakes.

Real-World Example of a Traditional Specification Document

Let’s examine a concrete example of a traditional software specification, it’s like a product requirements document from Lasoft. This is our Software Requirements Specification as a part of our documentation system. The case is with the actual client in the telecom industry under NDA. These docs usually include sections for functional requirements, user-centric perspectives (such as user stories or use cases), design artifacts (like UI wireframes), APIs, and acceptance criteria. Below, we outline a real-world specification document example from Lasoft, highlighting key elements to demonstrate how detailed and structured planning works in real software development. The actual client name is under NDA, but the document itself is a strong example of how software teams get aligned before any code is written.

The project involves building a mobile CRM tool for internal use, enabling the telecom company’s team to track client interactions, view contact histories, schedule follow-ups, and more.

The document opens with a clear Overview that defines the project’s subject and its aim: to streamline communication, improve internal sales workflows, and enable agents to interact with leads and contacts seamlessly from a mobile device.

From there, the team lists the Artifacts, provided by the client, including previous interface sketches and sample flows. These assets, often overlooked, are crucial: they show where the client’s vision already exists and provide the development team with a visual foundation to build on.

Client Artifacts:

• Previous UI sketches
• Sample contact workflows
• Feature wish list from internal staff, etc.

The goals section delves into the practical outcomes, for example, creating a central contact management hub, tracking deal statuses, integrating reminders and notes, and ensuring the app works on both iOS and Android. The goals are written in a way that bridges business needs and user behavior.

Then we navigate the system, defining user roles and expected behaviors. In this case, most users are internal team members who interact with clients or leads. The specification explains how users will view contacts, log communications, mark following actions, and even attach files or notes to client profiles.

User Roles:

• Sales agents (create/edit/view contacts)
• Team leads (assign and track follow-ups)
• Admins (manage user access and reporting)

One of the document’s strongest points is its section on software functionality and flows. Rather than dropping a giant wish list, the team uses detailed user stories to walk through functionality step by step.

The team also outlines the release plan with development stages. They map how many hours each segment will take (backend, mobile, QA, management), which helps forecast delivery timelines and costs.

Release Plan:

• Discovery, design, development, QA
• Time estimates for each module and team role
• Phased delivery across sprints (MVP-focused)

Choose Discovery from Lasoft

Lasoft offers flexible options for partnering.  Our clients usually consult with us to test their idea in the market and then decide whether to proceed with traditional software product development or vibe coding. In both cases, we encourage you to begin with the discovery stage to obtain a detailed specification and proceed accordingly. During the discovery, we’ll focus on gaining an idea of your product vision and its potential in the market.  Sessions and workshops with your stakeholders and team members will help us gain a deeper understanding of your goals, needs, and the desired outcome. These brainstorming sessions enable us to concentrate on priorities and identify potential market challenges for your product.

We’ll help you answer the following questions:

• The main idea behind the product and your vision for it’s value in the market;
• The profiles of potential users: their needs, pain points, and motivations;
• How users can interact with your product and what needs it can cover;
• The expected deliverables that the product is expected to provide.

This research enables us to evaluate how well the product aligns with market needs. Contact our expert to learn more about the discovery and obtain detailed specifications for efficient decision-making regarding budget, team structure, and whether it’s worth proceeding with product development.

Final Recap

Spec-driven development is becoming a key best practice in AI-augmented development. Instead of asking AI to write code based on a prompt, teams write a clear specification first. Business Analytics gives developers the user stories and acceptance criteria they need to work on. You can still get “hallucinations” when you use AI to make an app. This means AI can write code that doesn’t align with your real goals. In a spec-driven AI workflow, Business Analysts write detailed specifications that tell AI tools what to do.

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