Wednesday, November 01, 2023

FLOW Pattern: Comprehensive Knowledge Extraction with Large Language Models


The FLOW pattern aims to enable the systematic extraction, organization, and synthesis of comprehensive knowledge from large documents or transcripts using large language models, such as GPT-3. It addresses the limitation of these models' context window, allowing them to handle large documents by chunking them up in the different stages of the pattern.


Large language models often have a limited context window, which restricts their ability to ingest and analyze complete large documents at once. The FLOW pattern seeks to overcome this limitation by breaking down the document into manageable chunks, enabling a more comprehensive analysis and synthesis of information.


  1. Find: Extract specific topics or perspectives from the document or transcript in manageable chunks, considering the limitation of the model's context window.
  2. Link: Synthesize the extracted content from various chunks of the document or transcript, ensuring coherence and connectivity between different elements, while noting that the information comes from different parts of the source material.
  3. Organize: Structure the linked content in a logical and coherent manner, facilitating a systematic analysis of the document's core concepts and themes using the model's iterative capabilities.
  4. Write: Expand and elaborate on the organized content, producing a well-developed document that is informative, engaging, and accessible to the target audience, leveraging the model's text generation capabilities.


By implementing the FLOW pattern with large language models, organizations can effectively overcome the limitations of the context window, enabling a more comprehensive analysis of large documents and transcripts. This, in turn, enhances the model's ability to provide meaningful insights and make informed decisions based on a more holistic understanding of the underlying information.

Example Use Case:

An organization utilizes the FLOW pattern with a large language model like GPT-3 to create comprehensive meeting minutes from a transcript, effectively overcoming the context window limitation by chunking the document into manageable sections for analysis and synthesis.


The FLOW pattern, when implemented with large language models, serves as an effective approach for comprehensive knowledge extraction from large documents or transcripts. By addressing the context window limitation, it enables organizations to derive meaningful insights and make informed decisions based on a more comprehensive understanding of the underlying information.

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