Llm-Limitations

TL;DR LLMs are incredible force multipliers for building software. I’m building multiple projects concurrently with Claude Code. They excel at ideation, planning, and code generation when you do it right. But they struggle with predictable runtime behavior. They’re probabilistic systems at their core - you’ll get different results across runs no matter how much you prompt engineer. The variation isn’t a bug; it’s the architecture. Design for their nature, don’t fight it. They can be embedded in software when you account for variability. Great for chatbots, content generation, analysis work where you want different responses. ...

AI-assisted development tools are everywhere - systems that understand code, generate tests, fix bugs, and accelerate delivery. But practitioners are hitting a wall. Despite sophisticated prompts, quality controls, and multi-agent workflows, LLMs consistently cut corners on complex work. The problem isn’t training or prompt engineering - it’s architectural. TL;DR LLMs optimize for different objectives than human developers. The “laziness” comes from training to satisfy human approval patterns rather than correctness. My sophisticated workflows work against this optimization. ...