Build in a Day, Not a Month: My Experience with GitHub Spec Kit

From Requirements to Cloud Deployment Using AI-Driven Development

Over the last few months, I experimented with GitHub Spec Kit to evaluate how effectively it can support end-to-end software development. Rather than using it for a small prototype, I intentionally chose a fresh web application development scenario and attempted to drive the entire lifecycle using specifications as the primary input.

The result was impressive.

Starting with high-level business requirements, Spec Kit helped generate specifications, implementation plans, development tasks, application code, cloud deployment configurations, and deployment automation. While human oversight remained essential, the overall development experience felt significantly more structured and productive than traditional development approaches.

This article summarizes my observations, lessons learned, and practical recommendations for teams considering Spec Kit as part of their software engineering workflow.

What is GitHub Spec Kit?

GitHub Spec Kit introduces a specification-driven development approach where developers describe what needs to be built and AI-assisted tooling helps generate:

Product specifications Implementation plans User stories Development tasks Application code Deployment artifacts Execution tracking

Instead of starting with code, development begins with structured specifications that guide implementation throughout the project lifecycle.

Getting Started

The following example demonstrates a practical step-by-step approach to starting a new project with GitHub Spec Kit.

Prerequisites : First things first, before jumping in, let’s make sure we have the required tools in place:

1. **uv is installed** and configured on your machine.
2. You have access to an AI coding agent such as GitHub Copilot, Cursor, or Claude Code.

The real power of Spec Kit comes from a small set of commands that guide the project from requirements through implementation:

1. /speckit.constitution — command to create your project’s governing principles and development guidelines that will guide all subsequent development.

2. /speckit.specify — command that describes what to build, focusing on the what and why, not implementation details.

3. /speckit.clarify — command that help resolves unclear requirements through guided questions and refinement.

4. /speckit.plan — command that defines architecture decisions and tech stack for implementation.

5. /speckit.tasks — command that breaks the plan into actionable development tasks.

6. /speckit.implement — command that executes the tasks and builds the solution end-to-end.

That's it, with above mentioned command, I was taken on a full development ride, honestly, it felt like magic… Aaha!! It generated the code, provisioned Azure resources, created the database and tables, deployed everything, and even suggested what I could build next. And then… Boom!! Within no time, I had a fully working web application live on Azure, up and running, ready to test. This wasn’t theoretical or a demo — I actually did this myself.

Practical Example: Let’s assume we want to build an internal organizational platform where employees can collaborate, share knowledge, join interest groups, and participate in discussions. Following is sample that can be used

Step 1:

/speckit.specify Build a web application that enables employees to collaborate, share knowledge, and engage with peers across the organization through focused groups. The application should allow users to discover, browse, and join focused groups based on their interests. Each focused group should provide a dedicated space for conversations, announcements, and activities managed by designated leaders. The platform should support different user roles, including members, leaders, and administrators.

Step 2:

/speckit.clarify

Note —* The command presents a set of clarification questions along with multiple answer options, allowing the user to select the most appropriate response and proceed. In most cases, I was presented with around five such queries, each offering guided choices to refine the output.

Step 3:

/speckit.plan Create API-first implementation for the focused group backend using ASP.NET Core Web API. Start by establishing Project structure, API versioning, Routing conventions, Authentication integration, Role-based authorization, Standardized request/response contracts, Error handling

Step 4:

/speckit.tasks

Note — *Generate actionable task lists for implementation

Step 5:

/speckit.implement

Note — *Execute all tasks to build the feature according to the plan

My Experiment: Building a New Web Application

To evaluate Spec Kit properly, I started with a Greenfield project (a brand-new application). The objective was simple: Can a developer provide business requirements and successfully move from idea to cloud deployment using a specification-driven approach?

The answer was largely yes.

I provided high-level requirements, refined specifications through clarifications, generated plans and tasks, and then allowed implementation to proceed incrementally. The most interesting aspect was that Spec Kit maintained context across the lifecycle and continuously guided development based on previously generated artifacts.

Learnings:

1. The Development Experience

One of the strongest benefits I observed was the structured execution flow. Instead of manually creating:

Epics Features User Stories Technical Tasks Implementation Plans

Spec Kit generated them automatically from the specifications. The generated output looked remarkably similar to what many organizations maintain in Azure DevOps (ADO) or Jira. The tooling continuously provided visibility into:

What has already been completed What remains pending Recommended next actions Current implementation phase Task-level execution status

This created a development experience that felt more like managing a guided project than writing isolated code. For individual developers and small teams, this can significantly reduce planning overhead while maintaining project transparency.

2. Quality of Output Depends on Quality of Specifications

One of the most important observations was:

“The quality of output is directly proportional to the quality of specifications.”

When specifications were vague, implementation quality degraded. When requirements were comprehensive and detailed, the generated output improved dramatically. Detailed specifications resulted in:

Better architecture generation Better Epic and Feature decomposition More meaningful User Stories More accurate implementation tasks Better code generation More deployment-ready solutions

In many cases, detailed specifications brought the generated solution surprisingly close to production-ready quality.

3. Strong Visibility and Execution Tracking

Perhaps the most impressive capability was the execution tracking model. Traditional development often struggles with visibility because requirements, implementation tasks, and code exist in separate locations. Spec Kit connected these activities together. Throughout implementation, it provided:

Phase-wise progress tracking Task-level implementation visibility Remaining work identification Recommended next actions Clear execution sequencing

At any point, it was possible to understand:

What has been completed What remains pending What should be executed next

This level of transparency can significantly improve delivery management and project governance.

4. Human Calibration and Senior Engineering Oversight Remain Essential

While Spec Kit can significantly accelerate development, human calibration and experienced engineering oversight remain critical. At times, Spec Kit may take a technically valid but unintended path; however, with prompt corrections and guidance, it quickly adapts and proceeds in the right direction. In practice, this reinforces that —

Spec Kit is a powerful engineering assistant rather than a fully autonomous architect.

For example, in one POC scenario, the AI generated four separate database tables where a single, well-normalized table would have been more appropriate. Periodic human review ensures that while development speed increases, design quality and architectural integrity are not compromised.

5. Greenfield vs Brownfield Projects Require Different Approaches

Spec Kit performs particularly well for Greenfield projects where requirements and architecture are defined from scratch. Brownfield projects require additional context around existing systems, integrations, data models, and constraints. As a result, different specification and implementation strategies may be needed for new development versus modernization or migration initiatives.

6. Flexibility After Initial Implementation

An interesting observation was what happened after the initial implementation completed. Development did not stop. There were two natural continuation paths:

Option 1: Continue with Specifications — Generate additional specifications and continue expanding the platform in a structured manner.

Option 2: Guided Enhancement Mode — Use conversational prompts to incrementally enhance functionality. Many developers refer to this as “vibe coding,” where implementation evolves through iterative guidance rather than formal specifications.

Both approaches worked effectively depending on the situation. This flexibility allows teams to maintain rigor where needed while enabling rapid experimentation when appropriate.

7. Cloud Provisioning and Deployment Worked Surprisingly Well

One area where I expected challenges was cloud deployment. However, the experience was surprisingly smooth.

Spec Kit successfully:

Generated cloud resources Created deployment configurations Produced infrastructure definitions Automated deployment activities Repeated deployments consistently

In my case, Azure resources and deployment requirements were clearly defined, which significantly improved deployment outcomes.

8. Spec Kit Can Be Rigid

While the overall experience was positive, one limitation became apparent. Spec Kit sometimes follows a rigid implementation path. Once it commits to a particular interpretation, it may continue down that route even when alternative approaches might be preferable. Fortunately, this can usually be corrected through:

Additional clarification More specific prompts Architecture guidance Constraint definitions

Developers should expect to occasionally redirect implementation rather than assuming the first generated approach is always optimal.

Final Thoughts

GitHub Spec Kit represents a significant shift toward specification-driven software development. Based on my proof of concept, it can substantially accelerate:

Requirements decomposition Planning Task generation Application development Cloud deployment Execution tracking

However, successful adoption requires understanding a few realities:

Detailed specifications matter. Human review or Senior architectural oversight remains necessary. Greenfield and Brownfield projects require different strategies. AI-generated solutions should be challenged and refined when necessary.

The biggest takeaway from my experience is that Spec Kit is not replacing software engineers.

Instead, it allows engineers to spend less time creating project artifacts and more time making meaningful architectural and business decisions.

For teams willing to embrace specification-first development, GitHub Spec Kit offers a compelling glimpse into how software delivery may evolve over the coming years.

Happy Coding !!