You are probably prompting AI wrong: Anthropic philosopher explains how

As AI chatbots become increasingly integrated into various aspects of our daily lives, mastering the art of crafting effective prompts has emerged as a vital skill. Different AI companies offer diverse strategies for engaging with their models, leading to a plethora of techniques across platforms. Amanda Askell, a philosopher at Anthropic, has shared valuable insights on optimizing interactions with chatbots. In a Q&A video, she emphasizes the absence of a one-size-fits-all manual for prompting, describing it instead as an 'empirical domain.' This means that users must learn through observation and experimentation to identify the best prompting techniques. "Prompting is very experimental," Askell states, suggesting that new models require tailored approaches based on repeated interactions. She encourages users to abandon preconceived notions about how chatbots operate and to analyze the outputs they generate consistently. "It is really hard to distill what is going on because one thing is just a willingness to interact with the models a lot and to really look at output after output," she adds. Askell also highlights how her background in philosophy aids her in this process. She explains that her role often involves articulating complex issues or thoughts to the model in the clearest manner possible. In July, Anthropic published a Prompt Engineering Overview that included an analogy to help users navigate their chatbot, Claude. The company suggests envisioning Claude not merely as software, but as a brilliant, yet inexperienced employee who requires detailed instructions. Unlike a seasoned human colleague, Claude starts every interaction without any prior context about the user's preferences or working style. The guide emphasizes that the clarity and specificity of user instructions significantly influence Claude's performance. Just as a human can execute tasks more effectively with adequate context, the AI model will yield better results when provided with comprehensive information about user needs.