Definition
Prompt-based scraping is a data extraction approach where you write natural language instructions (prompts) that tell an AI model what information to extract from a web page. Instead of writing code with CSS selectors or XPath expressions, you describe your extraction goal in plain English and the AI interprets the page content to fulfill the request.
How It Differs from Traditional Scraping
Traditional Approach
Select all elements matching .product-card
For each, extract:
- .product-name text content
- .price span text content
- .rating data-score attribute
Prompt-Based Approach
Extract all products from this page. For each product,
get the name, price, and customer rating.
The traditional approach requires knowledge of the site's HTML structure. The prompt-based approach requires only knowledge of what data exists on the page.
Prompt Engineering for Extraction
Effective prompts for scraping follow certain patterns:
Be Specific About Output Format
Rather than "get the products", specify "extract each product as a JSON object with fields: name (string), price (number in USD), rating (number from 1-5)".
Provide Context
When extracting from ambiguous pages, add context: "This is an e-commerce product listing page. Extract only the main products, not related items or sponsored listings."
Handle Edge Cases
Instruct the model on what to do with missing data: "If a product has no rating, set it to null rather than guessing."
Advantages
- Low barrier to entry — no HTML or programming knowledge required
- Rapid iteration — changing what you extract is as simple as editing text
- Cross-site portability — the same prompt works on different sites showing similar content
Limitations
Prompts can be ambiguous, leading to inconsistent results. Complex extraction logic is harder to express precisely in natural language than in code. Output validation remains essential.
Prompt-Based Scraping in ScrapeGraphAI
ScrapeGraphAI supports prompt-based extraction as a first-class feature. You describe your extraction goals in natural language, and the platform handles the translation from intent to structured output. For more precision, you can combine prompts with explicit schemas.