How to Prompt AI for Web Scraping and Data Collection

TL;DR: AI can extract data from any public web page: lead directories, competitor pricing, job listings, reviews, contact pages. No code required. The prompts that work are specific about the URL, the fields to extract, and where the output should go.

A few years ago, extracting data from the web meant writing code or paying someone to write it for you. Now you describe what you want and an AI goes and gets it. And the timing matters: Deloitte's research found that real-time data is quickly becoming a baseline expectation for competitive businesses, not a nice-to-have.

The catch is that web scraping prompts have to be specific about the source, the structure, and where the output should go. Vague instructions produce vague results.

What can AI scrape and collect from the web?

Lead lists from directories. Job postings from company career pages. Pricing from competitor product pages. Customer reviews from G2 or Trustpilot. Contact details from event listing pages. And it can do this across multiple pages, with pagination, on a schedule.

What we see working best is pairing scraping with a Routine. A one-off scrape gives you a snapshot. A weekly scrape on a schedule gives you a trend. With Strawberry, your companion actually browses the live web (not cached training data), so the prices, listings, and reviews it pulls are current. Set it up as a Routine and it checks automatically without you asking.

What makes a scraping prompt work?

Three things. A clear URL or description of where to look. A specific list of which fields to extract. A destination for the output, usually a Google Sheet.

The prompts that fail say "scrape this website for useful information." The AI doesn't know what useful means to you. Strawberry automatically stores your scraping preferences in memory, so if you've previously set up similar extractions, your companion already knows your preferred fields and output format.

What are the best AI prompts for web scraping?

These are the prompts we see working consistently for data collection workflows using Strawberry.

Building a lead list from a directory

I want to scrape a company directory for leads. If you don't already know the URL and what information to extract (company name, industry, size, website), ask me. You might want to check how many pages the directory has before starting. Pull the results into a Google Sheet.

The "check how many pages" line is worth including every time. Some directories have 50 pages. Knowing that upfront changes how you approach the task.

Tracking competitor pricing

I want to monitor pricing on competitor product pages. If you don't already know which competitors and products to track, ask me. Visit each page, pull the current prices, and log them with today's date into my pricing tracker. You might want to flag anything that looks like a recent change.

Set this up as a Routine and it runs weekly without you having to ask. Over time you build a log of how competitor pricing has shifted.

Pulling job postings from career pages

I want to collect job postings from a set of company career pages. If you don't already know which companies to check and what role types to look for, ask me. Go through each career page and extract open roles with their title, location, and link. Add everything to a sheet.

Useful for competitive intelligence (tracking what a competitor is hiring for signals where they're investing) and for recruiting (monitoring whether a target company has opened a role).

Scraping and summarising competitor reviews

I want to analyse customer reviews for a product on a review site. If you don't already know which product and platform to check, ask me. Scrape the most recent reviews and pull out the key themes people mention most often. You might want to look at both positive and negative reviews separately.

The output is a structured themes summary that would take hours to produce manually and is usually more honest than anything in a competitor's own marketing.

Extracting contacts from a listing or event page

I want to extract contact information from a web page or listing. If you don't already know the URL and what fields to capture (name, email, company, role), ask me. Scrape the data and add it to a sheet. You might want to check whether the page uses pagination before starting.

Setting up a page monitoring alert

I want to monitor a web page for new content. If you don't already know the URL and what type of change to watch for (new listings, press releases, pricing updates), ask me. Check the page now to record the current state. You might want to set this up as a Routine so it checks automatically on a schedule.

How do you get better results from AI scraping prompts?

Be specific about the fields. "Extract company information" is vague. "Extract company name, website URL, number of employees, and HQ location" is not. The more specific the list, the cleaner the output.

Tell the AI where to put the data. Specifying the destination sheet means the data lands somewhere you can actually use it rather than in a chat window you'll lose.

For ongoing monitoring, use Routines. The difference between a one-off scrape and a weekly scrape running on a schedule is the difference between a snapshot and a trend.

We've written similar guides for researchers and sales teams where data collection is part of a broader workflow.

What's the best AI tool for web scraping without code?

Most AI tools don't browse the live web. They generate text based on training data, which means they can't pull current prices, today's job listings, or this week's reviews.

Strawberry Browser is an AI browser. Your companion actually visits web pages, reads what's there, and extracts what you need. It can run scraping tasks on demand or on a recurring schedule with Routines.

No code required. Try it at strawberrybrowser.com.