🔍 Generative Engine Optimization (GEO): My Journey into Making AI Understand My Content

Akhil Gusain — Tue, 05 Aug 2025 07:31:35 +0000

Introduction
A few months ago, I noticed something strange. I asked ChatGPT to recommend blogs on a topic I had written about—and it didn’t mention mine. Not even once. Despite being well-written, SEO-optimized, and published on Medium.

That’s when I stumbled upon a new concept: Generative Engine Optimization (GEO). It’s like SEO, but for AI. Instead of optimizing for search engines like Google, you optimize for AI models that generate answers, summaries, and recommendations.

This article is my personal journey into understanding GEO, experimenting with it, and even building a small Spring Boot-based tool to help content creators like me become more “AI-visible.”

🧠 What Is Generative Engine Optimization (GEO)?
GEO is the practice of structuring and tagging your content so that AI models can understand, retrieve, and recommend it when users ask questions.

Unlike traditional SEO, which relies on keywords and backlinks, GEO focuses on:

Semantic clarity
Structured metadata
Contextual relevance
Model-friendly formatting
In short, it’s about making your content “talk” to AI.

🧪 My First Experiment
I took one of my older blog posts and rewrote it with GEO principles:

Added clear headings and summaries
Used consistent terminology
Linked to authoritative sources
Added structured metadata using JSON-LD
Then I asked ChatGPT:

“What are some good resources on AI-powered music recommendation systems?”

This time, my blog showed up. That was the moment I realized GEO isn’t just theory—it works.

🛠️ Spring Boot Project: GEO Analyzer
To help myself and others, I built a simple Spring Boot app called GEO Analyzer. It scans a blog post and gives feedback on how “AI-friendly” it is.

🔹 Features:
Accepts raw blog content or URL
Analyzes structure, headings, and semantic clarity
Suggests improvements for AI discoverability
Generates JSON-LD metadata for embedding
🔹 Tech Stack:
Spring Boot (REST API)
OpenAI API (for semantic analysis)
Jsoup (for HTML parsing)
Thymeleaf (optional frontend)
🔹 Sample Endpoint:

@PostMapping("/analyze")
public ResponseEntity<GEOReport> analyzeContent(@RequestBody String content) {
    GEOReport report = geoService.analyze(content);
    return ResponseEntity.ok(report);
}

🔹 Output Example:


{
  "score": 78,
  "suggestions": [
    "Add a summary paragraph at the top",
    "Use consistent terminology for key concepts",
    "Include structured metadata using JSON-LD"
  ]
}

📈 What I Learned
AI models don’t “crawl” content like search engines—they predict based on patterns.
If your content isn’t structured clearly, it gets lost in the noise.
GEO is still evolving, but it’s going to be critical for creators, marketers, and developers.
🧭 Next Steps
I’m planning to:

Open-source the GEO Analyzer
Integrate it with Medium and Dev.to APIs
Add support for multilingual content
🧩 Final Thoughts
GEO isn’t just a buzzword—it’s a shift in how we think about visibility. In the age of AI, being searchable isn’t enough. You need to be generatable.

If you’re a developer, writer, or entrepreneur, I highly recommend exploring GEO. It’s not just about ranking—it’s about relevance in the AI-first world.

🤖 Why I Sometimes Regret Using AI Coding Assistants—A Developer’s Honest Take

Akhil Gusain — Tue, 05 Aug 2025 06:48:56 +0000

Introduction

I’ll admit it: I was one of the early adopters of AI coding assistants. When GitHub Copilot launched, I jumped in. When GPT-4 became available, I integrated it into my workflow. And for a while, it felt like magic.

But over time, I started noticing cracks. Not in the tools themselves—they’re brilliant—but in how they were affecting me as a developer.

This isn’t a rant. It’s a reflection. If you’re using AI to write code, or thinking about it, here’s what I wish someone had told me.

1. I Stopped Thinking Deeply About My Code
One of the first things I noticed was how quickly I started relying on suggestions. I’d type a function name, and boom—Copilot would fill in the rest. It was convenient, but I wasn’t designing anymore. I was just accepting.

There were times I didn’t fully understand the code I deployed. It worked, sure. But I couldn’t explain why it was the best solution. That bothered me.

2. My Debugging Skills Got Rusty
AI assistants are great at writing code, but not always at debugging it. When something broke, I found myself staring at the screen, waiting for the assistant to fix it. I wasn’t tracing logic or checking edge cases—I was outsourcing my problem-solving.

Eventually, I realized I was losing the muscle memory of debugging. And that’s dangerous.

3. I Missed Learning Opportunities
Before AI, every bug was a lesson. Every Stack Overflow rabbit hole taught me something new. But with AI, I often got the answer instantly—and skipped the learning.

I remember a time when I asked GPT to write a regex for me. It did. It worked. But I still don’t understand regex. That’s on me, but it’s also a side effect of instant gratification.

4. It Made Me Overconfident
There’s a weird paradox: AI makes you feel smarter, but it can also make you careless. I started deploying code faster, skipping reviews, assuming the AI had it covered.

Spoiler: it didn’t. I shipped a bug that cost us a few hours of downtime. The code looked clean, but it wasn’t tested properly. That was a wake-up call.

5. It’s Not Great at Architecture
AI can write functions, classes, even modules. But when it comes to system design, it struggles. I tried using GPT to help me design a microservices architecture. It gave me a decent starting point—but missed key things like data consistency, observability, and deployment strategy.

I realized that architecture isn’t just about code—it’s about trade-offs, team dynamics, and long-term thinking. AI isn’t there yet.

Conclusion
I still use AI coding assistants. They’re incredible tools. But I’ve learned to treat them like junior developers—not senior engineers. They need supervision, context, and review.

If you’re a developer, my advice is simple: don’t let AI replace your thinking. Use it to accelerate, not automate your brain.

DEV Community: Akhil Gusain

🔍 Generative Engine Optimization (GEO): My Journey into Making AI Understand My Content

🤖 Why I Sometimes Regret Using AI Coding Assistants—A Developer’s Honest Take