The Accordion Pattern: Why I stopped writing one fat LLM prompt
The author describes moving away from using a single, large prompt for extracting structured data from long documents due to reliability and performance issues. Instead, they adopted the 'Accordion Pattern,' which splits the task into two stages: first segmenting the document, then processing each segment individually. This approach improves accuracy, enables parallel processing, and isolates errors, making it more scalable for long inputs.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3903757) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Hideki Mori Posted on Apr 29 The Accordion Pattern: Why I stopped writing one fat LLM prompt #llm #ai #architecture #api Most structured-extraction tutorials look the same. Take a document, write one big prompt that says "extract A, B, C, D, E, F", get JSON back. Done. This works on short inputs. It quietly breaks on long ones. After running this in production for a while, I stopped doing it. Here's what I switched to and why.
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