Yes, use AI to move faster—but don’t let a working result stand in for understanding it. Kay Macfoy’s account of building a cloud project shows why: AI helped produce code and troubleshoot problems, yet configuration and deployment mistakes remained difficult to diagnose until the underlying system was read and checked.
What happened when AI helped build the project?
In a September 30, 2026 essay, Kay Macfoy describes starting the Cloud Resume Challenge in 2025. The challenge had 16 steps, as Macfoy recounts them. AI helped create the initial HTML, but Macfoy says ChatGPT chose Node.js even though the challenge specified Python. The project appeared to progress, but a visitor counter that initially worked later failed.
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Macfoy eventually found that the Azure Function responsible for the counter was missing required storage configuration. After correcting it, the counter recovered. Later, a separate issue in Step 11 arose: Macfoy says the deployment workflow in deploy.yml was configured incorrectly. That broke the counter while the site itself stayed online.
The episode is a personal account, not an independent audit of the code, Azure setup, certification, or costs. It does not establish that AI generally causes cloud failures. Its point is narrower and more useful: generated commands and plausible fixes can help without giving a builder a clear picture of how the pieces fit together.
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Why can a working result still be a problem?
A site loading successfully proves only that it works under the conditions tested. It does not prove that its owner understands which service handles a request, what configuration it needs, or what a deployment change will affect. As Macfoy puts it, “The problem was that ‘working’ and ‘understood’ are not the same thing.”
That gap matters most when something changes or breaks. In Macfoy’s story, the site could remain online while its counter failed; fixing the immediate symptom was not the same as understanding the Azure Function or the workflow that deployed it. If you cannot explain what a suggested command changes, or how to verify the result, you may be accumulating a system you cannot safely maintain.
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Where did AI help—and where did it fall short?
Macfoy’s essay is not an argument to reject AI. The author reports using it for initial HTML, troubleshooting, tests, dependency updates, and infrastructure as code. The risk was treating suggestions or a passing result as sufficient understanding. The useful distinction is between using AI to accelerate work and relying on it as a substitute for reading the project’s requirements, configuration, and generated infrastructure.
The account also describes Macfoy’s route to AZ-104 certification: four attempts over two years. That is one person’s experience, not evidence that a particular study method or AI tool produces certification success. It does reinforce that cloud knowledge takes sustained learning beyond getting a project to deploy.
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How did reading and checking change the process?
Macfoy describes moving from repeated troubleshooting prompts toward inspecting what the project actually contained and what each resource was for. That examination led to infrastructure changes and cost reductions in this particular project. The author reports an initial Azure bill of around $75 per month, later reducing roughly $74 to about $3 after removing resources, with further savings after removing Azure Front Door. These are approximate personal figures, not a pricing estimate for other Azure deployments.
The author also reports adding eight automated tests covering counter increments, initialization, CORS, unsupported methods, missing environment variables, and failure conditions. After dependency upgrades, an npm audit reportedly found zero known vulnerabilities; that result does not establish that the project had no vulnerabilities of any kind. Macfoy further reports validating an exported ARM template of 3,813 lines, using a what-if deployment, and testing in a disposable environment before treating infrastructure changes as finished. These are steps reported by the author, not independently reproduced results.
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A practical way to use AI without losing understanding
- Start with the requirement. Read the challenge, project brief, or service documentation yourself. Check that generated code and tool choices fit the stated requirements rather than assuming a plausible answer is compliant.
- Ask for explanations as well as output. For a code or configuration change, ask what it does, what it depends on, and what could fail. Treat the explanation as a starting point to verify, not as proof.
- Inspect the real configuration. When a feature fails, trace the components it depends on: for example, the function, its required settings, and the deployment workflow. A prompt response is not a substitute for checking those files and settings.
- Test the behavior and failure paths. Cover expected behavior as well as relevant edge cases, such as missing settings or unsupported requests. A successful page load alone may not exercise a backend feature.
- Review infrastructure changes before applying them. Understand which resources a template creates or changes, use available preview or what-if checks, and validate changes in a disposable environment when appropriate. Do not apply generated infrastructure merely because it parses or looks complete.
- Check ongoing resource use. Identify what each cloud resource does and whether the project still needs it. Macfoy’s savings followed removal of resources in one project; the appropriate resources and costs will differ elsewhere.
When should you pause before accepting an AI answer?
- You cannot explain what a command, setting, or code change will alter.
- The output conflicts with the project requirements or with the language, service, or version you intended to use.
- A fix makes one visible symptom disappear, but you have not checked dependent services or deployment configuration.
- You are about to change cloud infrastructure without knowing which resources will be created, modified, or removed.
- The result has not been tested against the behavior and failure cases that matter to the feature.
When one of these applies, stop prompting for another patch and read the relevant code, configuration, and requirements. Macfoy describes the shift this way: “It was learning when to stop prompting and start reading.”
What is the sensible answer to “Should you use AI?”
Use it as an assistant, not as the owner of the system. Macfoy’s experience includes real benefits as well as errors and hard-to-understand failures; it supports neither a blanket rejection of AI nor the idea that generated work can be trusted without review. The author’s concise advice is: “Use AI to move faster.” The condition is to keep reading, testing, and checking until you can tell what the project does and why.
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Read Kay Macfoy’s essay on DEV Community.
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