Ask HN: How is the community using LLMs for data cleaning/enriching/structuring?

Would love to learn how the community using LLMs for data wrangling and exchange prompts [1]. For example, we iterated several times on the following &quot;data structuring&quot; prompt for our Github Stargazers app [2,3]:<p>--- Prompt to GPT-3.5<p>You are given a block of disorganized text extracted from the GitHub user profile of a user using an automated web scraper. The goal is to get structured results from this data. Extract the following fields from the text: name, country, city, email, occupation, programming_languages, topics_of_interest, social_media. If some field is not found, just output fieldname: N&#x2F;A. Always return all the 8 field names. DO NOT add any additional text to your output. The topic_of_interest field must list a broad range of technical topics that are mentioned in any portion of the text. This field is the most important, so add as much information as you can. Do not add non-technical interests. The programming_languages field can contain one or more programming languages out of only the following 4 programming languages - Python, C++, JavaScript, Java. Do not include any other language outside these 4 languages in the output. If the user is not interested in any of these 4 programming languages, output N&#x2F;A. If the country is not available, use the city field to fill the country. For example, if the city is New York, fill the country as United States. If there are social media links, including personal websites, add them to the social media section. Do NOT add social media links that are not present. Here is an example (use it only for the output format, not for the content):<p><pre><code> name: Pramod Chundhuri country: United States city: Atlanta email: pramodc@gatech.edu occupation: PhD student at Georgia Tech programming_languages: Python, C++ topics_of_interest: PyTorch, Carla, Deep Reinforcement Learning, Query Optimization social_media: https:&#x2F;&#x2F;pchunduri6.github.io </code></pre> ----<p><pre><code> [1] https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Data_wrangling [2] https:&#x2F;&#x2F;github.com&#x2F;pchunduri6&#x2F;stargazers-reloaded [3] https:&#x2F;&#x2F;medium.com&#x2F;evadb-blog&#x2F;stargazers-reloaded-llm-powered-analyses-of-your-github-community-aef9288eb8a5</code></pre>