When scraping orchestration is the wrong abstraction for LLM workflows
The article discusses the challenges of using scraping orchestration for LLM workflows. It highlights the mismatch between the complexity of scraping platforms and the simpler needs of many LLM applications. The author suggests designing tools that provide predictable results without unnecessary abstractions.
- ▪Many LLM workflows require fresh data from web pages, leading to complex integrations.
- ▪Scraping platforms often include features that are not needed for simpler LLM tasks.
- ▪The article advocates for a more straightforward interface that focuses on data extraction rather than full scraping lifecycle management.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3930974) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Anakin Posted on Jun 3 When scraping orchestration is the wrong abstraction for LLM workflows #llm #webscraping #api #architecture A lot of LLM workflows start with the same small problem: the model needs fresh data from a web page. Then the integration grows sideways. You add a scraper, a queue, a dataset store, polling logic, retries, and a parser. By the end, the code that moves data around is larger than the code that uses the data.
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