WebDevPro #131: Building Reliable Applications with Cursor: A Spec-Driven Workflow for Incremental Development

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Welcome to this week’s issue of WebDevPro!

If you’ve been experimenting with tools like Cursor, you’ve probably had that moment where the output looks promising but not quite right. You tweak the prompt, try again, and end up chasing the result instead of building something stable.

What tends to work far better is not bigger prompts or more detailed instructions, but a shift toward a spec-driven, incremental workflow. One that mirrors how experienced developers already build systems, but adapts it to work effectively with AI-assisted coding environments.

In this article, we want to walk through a more reliable way to approach this. By the end, you’ll have a practical workflow for structuring your projects, breaking down features, and working with AI coding tools in a way that actually scales beyond small experiments.

Why direct prompting breaks down in real projects

It is tempting to treat Cursor as a high-powered code generator. Describe the app, mention the tech stack, and expect a complete implementation.

That approach holds up for small, disposable prototypes. It starts to fracture as soon as the project has multiple features, shared state, or evolving requirements.

There are a few reasons for this.

• First, ambiguity compounds quickly. Even a well-written prompt leaves room for interpretation. The model fills in those gaps based on patterns, not your intent.

• Second, outputs are inherently variable. The same prompt can produce slightly different structures, naming conventions, or flows. This makes it difficult to build predictably.

• Third, you give up control of the system’s shape. Instead of designing the architecture, you are reacting to whatever the model generates.

The result is a loop of correction rather than a process of construction.

A more reliable approach: constrain, decompose, iterate

A more effective pattern is to treat the tool as a collaborator that works best within clearly defined boundaries.

The workflow is straightforward:

• Define what the system should do

• Break it into discrete units of work

• Implement those units incrementally

• Refine through feedback

This is familiar territory. The difference is that your artifacts, like specifications and task lists, are now also guiding the model.

Start with a specification that removes guesswork

Before opening Cursor, it helps to define how the application should behave in concrete terms. Consider a simple example: a math practice application for children.

At a glance, it sounds trivial. In practice, it involves a number of decisions:

• What kinds of operations are supported

• How questions are presented

• How users interact with answers

• How incorrect attempts are tracked

• What metrics are surfaced in a statistics view

Writing this down in a structured format forces clarity. It also creates a shared reference point for every subsequent step.

One useful approach is to draft the specification in Markdown using a general-purpose language model. The key is not the initial output, but the interaction that follows. Asking the model to clarify uncertainties before generating the spec often leads to better results.

Once you have a draft, it is worth reviewing line by line. Fill in gaps, remove contradictions, and add constraints that matter to you. Visual expectations, state transitions, and edge cases are all worth capturing early.

The specification becomes less of a document and more of a contract. It defines intent in a way that both you and the tool can consistently refer back to.

Translate the specification into a phased TODO list

With a clear specification in place, the next step is to decompose it into manageable units of work.

Instead of thinking in terms of “build the app,” the focus shifts to “implement the next smallest meaningful piece.” A structured TODO list works well for this. Features are grouped into phases, and each phase contains tasks that can be completed independently.

For example:

Phase 1: Project setup

• Initialize React application

• Configure styling system

• Create base routing and placeholder pages

Phase 2: Core interaction

• Generate math questions

• Render question cards

• Implement answer selection logic

This breakdown serves two purposes: It reduces cognitive load. You are no longer juggling the entire system in your head. It also gives Cursor a much clearer target. Instead of interpreting a broad request, it can focus on a well-defined task.

Build incrementally and keep the scope tight

With the TODO list in place, you can begin implementing features in Cursor. The important shift here is how you frame your prompts. Rather than asking for a full implementation, you anchor the request to specific artifacts and a limited scope:

Use the specification and TODO list to complete Phase 1. Mark completed tasks.

This keeps the model grounded. It knows where to look for context and what success looks like. In the case of the math practice app, the first phase might result in:

• A scaffolded React project

• Basic routing

• Placeholder pages such as Home, Practice, Stats, and Settings

At this stage, the application is simple, but functional. That is intentional. Each phase establishes a stable base for the next.

Iterate deliberately instead of restarting

Once the initial output is in place, the next step is refinement.

It is common for the first version of the UI or structure to feel underdeveloped. The instinct might be to rewrite the prompt or regenerate the feature from scratch. A more effective approach is to iterate on top of what already exists.

For example:

Improve the homepage layout. Add clearer navigation and better spacing.

This kind of prompt is focused and contextual. It builds on existing work rather than replacing it.

Over time, this creates a steady progression:

• Initial structure

• Incremental improvements

• Targeted fixes

Each step is small, but collectively they move the system toward a more polished state.

Designing for variability instead of fighting it

One characteristic of working with language models is that outputs are not perfectly consistent. Even with the same inputs, you may see variations in structure, naming, or implementation details. Rather than trying to eliminate this variability, it helps to account for it in your workflow.

Specifications anchor intent.
TODO lists constrain scope.
Incremental development limits the blast radius of changes.

Together, these reduce the impact of variation. You retain control over the system, even as the underlying outputs shift slightly.

A closer look at a single feature

To make this more concrete, consider the first phase of the math practice application. The goal is to establish the basic application shell. You begin with two inputs:

• A specification that defines pages and navigation

• A TODO list that outlines setup tasks

The prompt is simple and scoped:

Complete Phase 1 using the provided specification and TODO list.

Cursor initializes the project, sets up dependencies, and creates the necessary files. You run the application locally and see a basic interface with navigation between pages. At this point, the system is functional but minimal. You then refine:

Improve the visual structure of the homepage and navigation.

The tool updates components, adjusts layout, and introduces better spacing. You review the changes, keep what works, and continue. This pattern repeats for each feature. The system grows in layers, each one built on a stable foundation.

Patterns that hold up in larger projects

A few patterns consistently make this approach effective. Clear specifications reduce the need for corrective prompts later. They shift effort from fixing outputs to shaping intent early.

Breaking work into smaller tasks improves reliability. Each unit is easier for the model to interpret and implement correctly.

Iteration keeps progress steady. Instead of waiting for a perfect result, you move forward through a series of improvements.

Documentation becomes an active part of the process. Specifications and TODO lists evolve alongside the code, keeping everything aligned.

Most importantly, you remain in control of the system’s design. The tool accelerates execution, but the direction still comes from you. But this workflow is not automatic. It depends on a few habits.

If the specification is vague, the outputs will reflect that.
If tasks are too large, the results become inconsistent again.
If changes are accepted without review, small issues compound over time.

The tool amplifies the process you bring to it. Structure leads to leverage. Lack of structure leads to friction.

Closing thoughts

Working with Cursor is less about mastering prompts and more about shaping a reliable development process. Once you move away from one-shot generation and toward a spec-driven, incremental approach, the experience changes. The tool becomes more predictable. The codebase becomes easier to reason about. Progress feels steadier.

In many ways, this is not a new way of building software. It is a return to fundamentals, adapted for a new interface. The difference is that when the structure is right, the speed at which you can move begins to compound.

If you want to go deeper into this way of working, especially with hands-on examples and a full project walkthrough, this approach is explored in detail in Vibe Coding with Cursor. It’s a practical guide to building real applications using these patterns, and a useful next step if you’re looking to move beyond experimentation into something more structured.

This Week in the News

• 🤖 OpenAI to acquire Astral: OpenAI is acquiring Astral and its widely used open-source Python developer tools, including uv, Ruff, and ty. The move strengthens OpenAI’s push into developer tooling, bringing core workflows like dependency management, linting, and type checking closer to its ecosystem. The goal is to accelerate Codex beyond code generation, positioning it to operate across the full development lifecycle, where it can work directly within the tools developers already use.

• 🗓️ Why JavaScript still struggles with dates: The JavaScript new Date(someString) constructor is notoriously over-eager, stemming from legacy C++ parsers in engines like V8 and SpiderMonkey that aggressively “guess” date components to maintain backwards compatibility. This leads to absurd “hallucinations” where the engine prioritizes finding a year at any cost. Beyond these quirks, the parser’s inconsistency is a frequent source of production bugs; for instance, new Date(”2024-03-25”) is treated as UTC, while adding a time element like T00:00 shifts it to Local Time, often causing “off-by-one-day” errors for users in different time zones. To avoid silent data corruption, such as new Date(null) defaulting to the 1970 Unix Epoch, developers should abandon these loose string constructors in favor of strict ISO 8601 formatting or the modern, predictable Temporal API.

• ▲ Next.js 16.2 updates: Next.js 16.2 introduces a set of refinements focused on improving performance, stability, and the overall developer experience. The release builds on recent changes to the app router and server components, with updates that make common workflows more predictable and easier to manage. The direction remains consistent, with Vercel continuing to smooth out rough edges while aligning Next.js more closely with modern React patterns and production needs.

• ⚡Vite 8 and Void, the fill-stack pivot: The Vite ecosystem is expanding beyond build tooling. Alongside the release of Vite 8, which brings updates focused on faster builds and a more streamlined developer experience, the VoidZero team has introduced Void, a Vite-native deployment platform built on Cloudflare Workers. Void aims to turn Vite apps into full-stack applications by bundling capabilities like databases, KV storage, object storage, and AI inference directly into the workflow. Together, these updates signal a shift toward a more integrated Vite stack that spans both development and deployment.

• ⚖️ No AI code in Node.js core? A new petition sparks debate: A long-running discussion around AI-assisted development in OpenJS Foundation projects has taken a new turn. A Node.js contributor has started a petition urging the TSC to restrict AI-generated code from being merged into Node’s core, following concerns raised alongside a large 19K LOC PR. The debate touches on deeper questions around code quality, maintainability, and ownership as AI becomes a common part of development workflows.

• 🧩 How TanStack approaches modern developer tooling: TanStack shares a set of ecosystem updates alongside a detailed talk that breaks down the thinking behind its tooling approach. The focus stays on building framework-agnostic, composable primitives that can scale across different application architectures without locking developers into a specific stack. The talk offers a deeper look into how these decisions play out in practice, from managing server state to structuring UI logic, and why flexibility and interoperability remain central to TanStack’s design philosophy.

Beyond the Headlines

• 📉 The hidden cost of “comprehension debt”: Addy Osmani introduces the idea of comprehension debt: the growing gap between how quickly code can be produced and how easily it can be understood. As tools (especially AI) accelerate code generation, the burden shifts to developers who need to read, debug, and maintain that code. It’s a useful lens for thinking about long-term code quality. Fast output is valuable, but only if teams can still reason about what they’ve built.

• 🧠 React to Svelte migration with AI agents: Strawberry shares how it rewrote 130K lines of React code to Svelte in just two weeks using coding agents, cutting UI complexity and doubling performance in the process. The write-up goes deeper into why the team moved away from React’s rendering model toward Svelte’s compiler-first approach, and how AI-assisted workflows made a full rewrite practical at a scale that would usually take months. It’s a strong signal of how framework decisions, performance constraints, and AI-driven development are starting to converge in real-world systems.

• 🎥 Rethinking how we build modern web apps: This talk explores how modern web development is evolving, touching on architecture, tooling, and the trade-offs developers face as applications grow in complexity. It’s a useful watch if you’re thinking about how today’s patterns scale in real-world systems.

• 🚄 Understanding how JIT affects runtime performance: This post explores how Just-In-Time (JIT) compilation behaves in practice, looking at how execution performance evolves over time. It offers a deeper look at runtime optimizations and how they impact real-world performance characteristics.

Tool of the Week

🔍 Understand code changes with structural diffs

Traditional diffs compare text line by line, which can make complex changes hard to follow. Difftastic takes a different approach by comparing the syntax tree of your code, helping you see what actually changed at a structural level.

It supports multiple languages and highlights meaningful differences, making it especially useful when reviewing refactors or large code updates.

That’s all for this week. Have any ideas you want to see in the next article? Hit Reply!

Cheers!

Editor-in-chief,

Kinnari Chohan

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