The research paper A Survey on Large Language Models with some Insights on their Capabilities and Limitations provides a clear, comprehensive overview of how modern large language models like GPT, Claude, and LLaMA actually work - how they learn, reason, and adapt - and what their current limitations are. It explains that true intelligence in AI doesn’t come from scale alone, but from the balance of architecture, high-quality data, and thoughtful fine-tuning. For business leaders, it clarifies why some AI systems outperform others, where risks such as bias and hallucination arise, and how structured training and modular design can make enterprise AI more reliable and effective. In essence, it offers a roadmap for making smarter investment, governance, and deployment decisions in an era where AI capability is advancing faster than its understanding.

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Key point: This paper shows that large language model intelligence emerges not just from scale, but from the balance of architecture, data quality, and fine-tuning, making thoughtful design the key to reliable and capable AI systems.