AI has become an invaluable tool in software development. It’s fun to use, makes coding more efficient, and, most importantly, lowers the barrier to entering new domains.

For anyone exploring a new technology, AI-generated suggestions, code snippets, and explanations can rapidly accelerate learning. Need to write a web scraper, deploy a cloud function, or optimize a SQL query? AI can help you get started in minutes.

However, the magic fades when you step into less popular or unconventional territories—such as game development using Golang and Raylib. That’s when software development with AI transforms from a fun, smooth ride into a tricky endeavor filled with gaps and blind spots.

1. AI as an Onboarding Tool for New Technologies

For mainstream tech stacks—React, Django, Flask, Java Spring, Node.js—AI is like a supercharged personal mentor.

  • It can explain complex concepts in simple terms.
  • It provides working code snippets tailored to popular frameworks.
  • It suggests best practices based on industry standards.

For example, if you ask an AI tool to build a CRUD API in Django, you’ll get step-by-step instructions with relatively clean, production-ready code (most of the times). This dramatically speeds up the learning curve for beginners and experienced developers alike.

But what happens when you step into a less common development stack?

2. The Struggle of Using AI for Less Popular Tech (Like Golang + Raylib for Game Dev)

When working with mainstream tech, AI provides polished, well-tested answers. But when working with less popular frameworks or languages, things start breaking down.

The Problem: AI’s Knowledge is Based on Available Data

  • AI tools rely on patterns found in training data, which means they’re only as good as the available resources.
  • For common languages like Python, Java, and JavaScript, there’s a massive dataset to learn from.
  • For less popular combinations like Golang + Raylib, the AI has limited real-world examples, leading to incorrect or incomplete suggestions.

Example: Making a Game with Golang + Raylib

  1. AI Can Help with the Basics

    • Setting up a project.
    • Rendering a window with Raylib.
    • Handling basic input events.

  2. AI Struggles When You Need Depth

    • AI often suggests outdated or nonexistent functions.
    • It lacks examples of best practices for game architecture in Golang (although it was a bad choice on my end).
    • It may generate C-based solutions that don’t align well with idiomatic Go code.

  3. Debugging AI-Suggested Code Becomes a Hassle

    • You get stuck debugging incorrect assumptions rather than coding your actual game.
    • AI might provide simplistic answers instead of optimized, real-world solutions.

Example: Implementing a Scrollbar in an Immediate Mode UI

One example I had recenlty was when I wanted to add a scrollbar to my custom immediate-mode UI in Golang.

  • All the solutions from AI-generated code and Google searches were JavaScript + CSS-based.
  • No relevant examples existed for Raylib + Golang immediate mode UI.
  • I had to go back to basics—thinking, reading manuals, forum discussions, and open-source examples to figure out the correct approach.

Lesson learned?

AI is great when the solution already exists in a well-documented ecosystem. But when working in a niche or experimental area, there’s no substitute for hands-on research and problem-solving.

3. Using AI Wisely in Software Development

AI is not a replacement for experience, deep learning, and debugging skills—but it’s a powerful assistant when used correctly.

When AI is Extremely Useful

✅ Getting started in a new domain (as long as it’s popular).
✅ Writing boilerplate code (e.g., setting up a new API, handling authentication).
✅ Generating quick prototypes and testing concepts.
✅ Understanding existing code in a large project.
✅ Refactoring & debugging common issues.

When AI Struggles

❌ Working with niche technologies.
❌ Solving highly specific problems that require deep contextual knowledge.
❌ Making architectural decisions in unfamiliar frameworks.
❌ Optimizing performance beyond generic suggestions.

Rule of Thumb:

  • Use AI as a guide, but don’t blindly trust its output.
  • Cross-check AI-generated solutions against official documentation and community discussions.
  • Be prepared to debug AI’s mistakes—especially in unconventional projects.

4. Final Thoughts: AI is Great, But It Won’t Replace Deep Work

Software development with AI is a game-changer, but it has clear limitations.

  • AI accelerates entry into new technologies.
  • AI struggles with niche or under-documented areas.
  • AI won’t replace deep technical expertise—it just helps speed up the learning process.

For mainstream software development, AI is like a skilled co-pilot. But for niche or experimental projects, you still need to rely on deep understanding, hands-on debugging, and community resources.

So the next time AI struggles to give you a solid answer, don’t get frustrated—just remember that some paths still require good old-fashioned human problem-solving.