In AI-driven web scraping, accurately identifying and extracting facts from webpages can be a challenge. Whether it's determining if a page is a blog

Evaluating Webpage Fact Extraction with Braintrust - Part 1

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2024-10-22 21:00:06

In AI-driven web scraping, accurately identifying and extracting facts from webpages can be a challenge. Whether it's determining if a page is a blog post, press release, or directory, the data needs to be structured correctly for downstream applications. This is where evaluations come into play, allowing us to measure the accuracy and reliability of the extracted information. In this post, I’ll walk you through how we integrate Braintrust to evaluate prompt-driven web scraping and how we use a custom JSON scorer to ensure that entities and facts are extracted correctly from webpages.

Scraping is more than just grabbing HTML content—it's about identifying and extracting meaningful entities and facts. Here’s why evaluations are crucial:

Ensure accuracy: As we scrape and process data from web pages, we need to verify if the correct page type and details are extracted.

Improve reliability: Constant feedback on the scraping process helps refine the LLM prompts we use, making the system more robust over time.