The TRUTH about AI coding assistants : AI has a better chance of succeeding at more generic tasks instead of niche requests (think complex code). This is because LLMs leverage existing datasets. HERE’s WHY LLMs ‘suck’ at coding :

❌ Syntax Hurdles: LLMs struggle with strict syntax rules. Code requires precise formatting and punctuation, making it hard for models to consistently produce error-free code. Generated code may look correct but won't execute properly.

⚡ Contextual Ambiguity: LLMs can struggle to grasp code snippet meanings and intentions. Ambiguities in variable names, function usage, or program flow can lead to incorrect or nonsensical code suggestions.

🔍 Limited Training Data: LLMs lack large-scale, high-quality code datasets. This limits learning and accurate code generation. Training data primarily consists of natural language, not actual code.

🧠 Creativity vs. Accuracy: LLMs prioritize novelty over code correctness. This results in unconventional or inefficient code suggestions that might work theoretically but fall short in practice.

🛠 Future of AI in Coding: LLMs can and will get more accurate at generating code with time. Meanwhile, the key is synergy. Combining human expertise with the strengths of AI.

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