Think dumping 100,000 lines of code into ChatGPT o3 or Gemini 2.5's massive context window will magically fix all your bugs? Think again. I spent days testing this with large codebases (15k, 44k+ lines) hoping AI would solve everything – it doesn't work like that.
➡️ In this video, I share my experience and reveal:
• Why large AI context windows (ChatGPT o3, Gemini 1.5/2.5) fail for deep debugging and nuanced code analysis.
• The "illusion" of AI understanding complex, large codebases – it's not truly grasping everything.
• When large context is useful (hint: it's not for pinpointing complex bugs).
• Why RAG (Retrieval-Augmented Generation) isn't dead, despite massive context windows.
• The limitations of AI intelligence in coding – they're not as smart as we hope (yet!).
• My thoughts on "vibe coding" – maybe it is the right way to start?
• A potentially better strategy I'm testing: using Gemini 2.5 to generate a detailed test plan from the entire codebase, then using test-driven development (TDD) to fix the issues.
• Why Gemini's test plan approach felt superior to ChatGPT o3's attempt.
• The importance of TDD when working with AI, acknowledging AI code generation can be error-prone
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I was really hoping the "throw everything in" method would work, but current AI models seem more like tools needing careful guidance than genius programmers. This new Gemini-powered test plan approach might be the key. Let me know if you've tried something similar!
LinkedIn: https://www.linkedin.com/in/christopher-royse-b624b596/
#AICoding #LargeLanguageModels #ContextWindow #GeminiAI #Claude3 #SoftwareDevelopment #Programming #Debugging #TestDrivenDevelopment #VibeCoding[2][5][7]