The Accordion Pattern: Why I stopped writing one fat LLM prompt
The article discusses the limitations of using a single large prompt for extracting structured data from long documents. The author introduces the 'Accordion Pattern' as a more effective method that involves breaking the task into two stages: segmenting the document and then extracting data from each segment. This approach improves reliability, reduces latency, and allows for better error handling.
- ▪Using one large prompt for long documents often leads to quality issues and increased latency.
- ▪The Accordion Pattern involves two stages: first, segmenting the document, and second, extracting structured data from each segment.
- ▪This method allows for parallel processing and better error isolation, improving overall efficiency.
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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 Solo engineering notes (3 Part Series) 1 The Accordion Pattern: Why I stopped writing one fat LLM prompt 2 Nobody knows when a job will finish. I'd still like to report it accurately. 3 What survives when you build alone for 24 years Most structured-extraction tutorials look the same.
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