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  2. Prompt engineering - Wikipedia

    en.wikipedia.org/wiki/Prompt_engineering

    Prompt engineering is the process of structuring or crafting an instruction in order to produce the best possible output from a generative artificial intelligence (AI) model. [ 1 ] A prompt is natural language text describing the task that an AI should perform. [ 2 ]

  3. I work at Microsoft and teach a Stanford Online course on AI ...

    www.aol.com/news/microsoft-teach-stanford-online...

    This as-told-to essay is based on a conversation with Aditya Challapally, a 30-year-old Microsoft employee who teaches a course for Stanford Online about generative AI. This story has been edited ...

  4. OpenAI head of product shares 5 tips for using ChatGPT - AOL

    www.aol.com/openai-head-product-shares-5...

    OpenAI rolled out its latest AI model, GPT-4o, earlier this year. Many people use ChatGPT to create recipes or write work emails, but OpenAI's Head of Product Nick Turley has some handy tips users ...

  5. 13 Ways To Use AI To Become a Better Writer - AOL

    www.aol.com/13-ways-ai-become-better-144100048.html

    6. Explain complex topics in new ways. Generative AI can even help you better understand the topics you’re writing about, especially if the tool you’re using is connected to the internet.

  6. Wikipedia : Using neural network language models on Wikipedia

    en.wikipedia.org/wiki/Wikipedia:Using_neural...

    AI copyediting of Wikipedia text as of 2022 can slightly reduce the work copyeditors need to do. However, human supervision is critical when using such tools. This task heavily relies on prompt engineering in order for the AI to give satisfactory results. For me, I settled with the prompt "Can you copyedit this paragraph from Wikipedia while ...

  7. Reinforcement learning from human feedback - Wikipedia

    en.wikipedia.org/wiki/Reinforcement_learning...

    The reward model is first trained in a supervised manner to predict if a response to a given prompt is good (high reward) or bad (low reward) based on ranking data collected from human annotators. This model then serves as a reward function to improve an agent's policy through an optimization algorithm like proximal policy optimization. [3] [4] [5]