AI models generate content based

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Challenges in generating unique ideas Limitation: on patterns they’ve seen. While they can combine these patterns in novel ways, they don’t “invent” like humans do. Their “creativity” is a recombination of existing ideas. Data Privacy, Intellectual Property, and Quality Control Issues: Ethical consideration: Using AI models in applications that handle sensitive data raises concerns about data privacy. When AI generates content, questions arise about who owns the intellectual property rights. Ensuring the quality and accuracy of AI-generated content is also a significant challenge.

Bad code AI models might generate syntactically DB to Data code when used for coding tasks but functionally flawed or insecure. I have had to correct the code people have added to sites they generated using LLMs. It looked right, but was not. Even when it does work, LLMs have out-of-date expectations for code, using functions like “document.write” that are no longer considered best practice. Hot takes from an MLOps engineer and technical SEO This section covers some hot takes I have about LLMs and generative AI.



Feel free to fight with me. Prompt engineering isn’t real (for generative text interfaces) Generative models, especially large language models (LLMs) like GPT-3 and its successors, have been touted for their ability to generate coherent and contextually relevant text based on prompts. Because of this, and since these models have become the new “gold rush,” people have started to monetize “prompt engineering” as a skill. This can be either $1,400 courses or prompt engineering jobs.