<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>JohnFen</author><text>My experience with Google is the exact opposite. It is so poor at interpreting &amp;quot;what I really want&amp;quot; that for it to be really useful, I need to lean harder on google-fu than in the beforetimes. But Google has removed, weakened, or broken many of the operators that were used to do this, so google-fu isn&amp;#x27;t as possible as it used to be.</text><parent_chain><item><author>msoad</author><text>why can&amp;#x27;t the model do those things? As someone said, it&amp;#x27;s like Google-foo. It used to be the case where you had to give Google some context to disambiguate certain queries but now it is really good so I don&amp;#x27;t have to write anything crazy like site:wikipedia.com etc</text></item><item><author>krab</author><text>Not really. It&amp;#x27;s just a higher level programming language. You still need to know how to decompose a problem and structure the solution. On some level, also systems thinking as this stuff will get integrated together.</text></item><item><author>msoad</author><text>The whole prompt engineering thing feels like a temporary stopgap. Because ideally language models should be able to understand prompts from anyone without them trying to craft in a way that works for the model.</text></item></parent_chain></comment><story><title>Prompt Engineering Guide: Guides, papers, and resources for prompt engineering</title><url>https://github.com/dair-ai/Prompt-Engineering-Guide</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>rossvor</author><text>Huh, my recollection is the exact opposite. I remember the good old days when I could use inurl: link: and explore the website contents fully and drill down further if necessary, compared to now, where google seems to always think to know better than you what you are looking for. If you are not happy with the initial results it gave you, you are pretty much out of options, good luck trying to drill down to some specific thing.</text><parent_chain><item><author>msoad</author><text>why can&amp;#x27;t the model do those things? As someone said, it&amp;#x27;s like Google-foo. It used to be the case where you had to give Google some context to disambiguate certain queries but now it is really good so I don&amp;#x27;t have to write anything crazy like site:wikipedia.com etc</text></item><item><author>krab</author><text>Not really. It&amp;#x27;s just a higher level programming language. You still need to know how to decompose a problem and structure the solution. On some level, also systems thinking as this stuff will get integrated together.</text></item><item><author>msoad</author><text>The whole prompt engineering thing feels like a temporary stopgap. Because ideally language models should be able to understand prompts from anyone without them trying to craft in a way that works for the model.</text></item></parent_chain></comment><story><title>Prompt Engineering Guide: Guides, papers, and resources for prompt engineering</title><url>https://github.com/dair-ai/Prompt-Engineering-Guide</url></story>