This post is my submission for DEV Education Track: Build Apps with Google AI Studio.
What I Built
I built an EEAT Checker that analyzes website content for Google's Experience, Expertise, Authoritativeness, and Trustworthiness signals.
My primary prompt was: "Create a web app that analyzes URL content and evaluates it against Google's EEAT guidelines, providing scores for each pillar with actionable improvement suggestions."
I utilized Google AI Studio's Gemini model for content analysis and structured the output to include detailed scoring breakdowns and specific recommendations for improving each EEAT component.
Demo
Try the EEAT Checker: https://ai.studio/apps/drive/1oliEsFDIMerEH7n2g6YX3trplmMbYdXi
The app accepts any URL and returns:
Individual scores for Experience, Expertise, Authoritativeness, and Trustworthiness
Overall EEAT rating
Specific recommendations for improvement
Analysis of author credentials, content depth, and trust signals
My Experience
Building this tool was eye-opening on multiple fronts. As an SEO professional, I've been manually evaluating EEAT for months, but seeing Google AI Studio generate a functional checker in minutes was incredible.
The most surprising aspect was how well Gemini understood nuanced SEO concepts without extensive training—it accurately identified missing author bios, weak source citations, and thin content.
The prompt engineering process taught me that specificity matters. My first attempt was too generic ("check content quality"), but refining it to target specific EEAT components dramatically improved the results.
I also learned that Google AI Studio's built-in deployment is remarkably smooth—no dealing with hosting configurations or environment setup.
What stood out most was how accessible AI development has become. I went from idea to deployed tool without writing traditional code, which opens doors for SEO specialists, marketers, and content creators who have domain expertise but limited programming experience.
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