They excel at recognizing and generating

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LLMs have context from other people’s writing, making them useful for generating user stories or more nuanced programmatic ideas. Instead of recommending TVs to someone who just bought a TV, LLMs can recommend accessories someone might want instead. Controversy The use of AI in marketing raises privacy concerns. There’s also a debate about the ethical implications of using AI to influence consumer behavior. Dig deeper: How to scale the use of large language models in marketing Continuing issues with LLMS Contextual understanding and comprehension of human speech Limitation.

AI models, including GPT, often struggle with nuanced DB to Data interactions, such as detecting sarcasm, humor, or lies. Example: In stories where a character is lying to other characters, the AI might not always grasp the underlying deceit and might interpret statements at face value. Pattern matching Limitation: AI models, especially those like GPT, are fundamentally pattern matchers.  content based on patterns they’ve seen in their training data. However, their performance can degrade when faced with novel situations or deviations from established patterns.



Example: If a new slang term or cultural reference emerges after the model’s last training update, it might not recognize or understand it. Lack of common sense understanding Limitation: While AI models can store vast amounts of information, they often lack a “common sense” understanding of the world, leading to outputs that might be technically correct but contextually nonsensical. Potential to reinforce biases Ethical consideration: AI models learn from data, and if that data contains biases, the model will likely reproduce and even amplify those biases. This can lead to outputs that are sexist, racist, or otherwise prejudiced.