Best AI Prompts to Extract Information Free

Home

AI Prompt List

Text/Words

Extract information

Text/Words

154

This prompt is used to extract a person's full name and complete mailing address from the text of an email, commonly for data entry or contact management tasks.

Prompt Content

Copy

Extract the full name and complete mailing address from this email.

Copy

Share Click to copy the link

Comments (8)

0/300

Submit

StevenGonzalez

May 31, 2026 at 5:26:30 PM EDT

I tried this prompt on a messy email thread and it actually pulled the right name and address, so that's a win. But it choked when the address was split across two lines with no comma. Maybe add a note about handling line breaks? Just a thought.

WalterNelson

May 31, 2026 at 12:26:30 AM EDT

Tried this prompt on a messy email with a phone number and name all jumbled together. It pulled out the full name and address perfectly, even though the format was weird. Only complaint is it didn't keep the original line breaks. Saved me a lot of manual work though 👍

BruceWilson

May 30, 2026 at 8:26:31 AM EDT

This prompt is straightforward and gets the job done. I used it to pull addresses from a batch of customer emails, and it worked like a charm. Only wish it specified handling multiple names or addresses in one go. Otherwise, solid! 📧

MichaelMartínez

May 26, 2026 at 3:26:32 AM EDT

This prompt is super clear and does exactly what it says! Used it to pull addresses from customer emails and it worked flawlessly. The structure is simple but effective. Maybe adding a request to format the output as JSON could be a nice upgrade for developers? Overall, a solid and reliable tool for a specific task. 👍

JuanWhite

May 17, 2026 at 12:26:31 PM EDT

This prompt is super straightforward for pulling contact details from emails. I've used it a few times to organize customer inquiries, and it works like a charm. The structure is clear, but maybe adding a field for 'company name' would make it even more useful for business emails. Overall, a solid time-saver! 👍

Recommendation

E-commerce Product Short Title Optimization

Optimize existing product short titles provided by merchants based on the search preferences of mainstream e-commerce platforms. Keep the optimized title within the platform's character limit, accurately cover core product attributes, target users and core selling points, improve search matching, and let consumers quickly get key information. The optimized title needs to comply with platform rules and avoid illegal keyword stuffing.

E-commerce Product Title Optimization

Work with the given core attributes of an e-commerce product and current platform hot search terms, adjust the word order and combination of the existing product title, keep the core keywords, and polish a Chinese product title that fits the platform's search rules better and attracts more consumer clicks.

E-commerce Product Title Optimizer

Follow Taobao and JD search ranking rules to help small e-commerce merchants optimize existing product titles, keep core product attributes and keywords, adjust word order and integrate hot long-tail keywords to make the title fit platform preferences and attract more clicks, output an optimized title within 30 words and note the adjustment ideas.

E-commerce Product Short Copy Polishing

You polish and optimize short titles and selling point copies for retail products on e-commerce platforms, keep the product's core category, function and promotion information, adjust language fluency and wording appeal, adapt to the short reading habits of mobile consumers, meet the platform's title character limit requirements, and output a finished copy that is smooth, natural and attractive enough to get clicks from target buyers.

E-commerce Product Review Rewriting

Rewrite raw user positive reviews provided by e-commerce sellers into natural, authentic comments that highlight core product strengths, sound like regular buyers' speech, fit platform search preferences and can be directly used on product pages to improve conversion.

E-commerce Product Review Sentiment Summarization

Process user reviews of a specific product on an e-commerce platform, first sort each review into positive, neutral and negative sentiment categories, then extract core opinions mentioned by users under each category, merge identical or similar opinions, and finally organize them into a logically clear summary for merchants to quickly understand user feedback on this product.