The Intersection of Note-Taking Systems and AI Knowledge Orchestration


Hatched by Glasp

Aug 15, 2023

4 min read


The Intersection of Note-Taking Systems and AI Knowledge Orchestration


Note-taking systems have long been a popular method for organizing information and capturing thoughts. However, relying solely on elaborate note-taking systems may hinder the organic flow of ideas and original thinking. On the other hand, in the realm of artificial intelligence (AI), knowledge orchestration plays a crucial role in enhancing reasoning capabilities. This article explores the common points between note-taking systems and AI knowledge orchestration, highlighting the importance of simplicity in note-taking and the value of end-to-end interaction data in AI development.

The Essence of Note-Taking Systems:

While note-taking can be a valuable tool for capturing important information, it is essential to avoid making the act of note-taking the primary focus. The natural salience filter within our minds allows us to identify what truly resonates with us. By relying on this filter, we can capture ideas that organically stick in our minds, rather than adhering to preexisting criteria. The most thought-provoking writing often emerges from synthesizing previously unarticulated concepts in the present moment. Thus, note-taking should serve as an elementary practice that supports the creative process, rather than becoming the sole objective.

AI and Knowledge Orchestration:

AI's intelligence comprises two vital components: reasoning and knowledge. While advanced models like GPT-4 excel in reasoning, their knowledge of the world remains limited. This limitation creates a bottleneck in their performance, as they require the right knowledge at the right time to reason effectively. Knowledge orchestration, which encompasses storing, indexing, and retrieving relevant knowledge, is a significant challenge for AI builders.

Advancements in AI Knowledge Orchestration:

To address the knowledge orchestration challenge, developers are working on enhancing context window sizes. Models like GPT-4 now boast a 32,000 token context window, allowing for more comprehensive knowledge integration. Additionally, developer tools and infrastructure layers, such as LlamaIndex and Langchain, simplify the chunking, storage, and retrieval of knowledge from various databases. Vector database providers like Pinecone, Weaviate, and Chroma are actively competing in this field, with Pinecone leading the way.

The Value of End-to-End Interaction Data:

One type of knowledge that holds immense value is end-to-end interaction data about the lifecycle of any process. Whether it pertains to a company's internal tasks or customer-enabled activities, end-to-end interaction data provides insight into the entire process, from its inception to the final results. This comprehensive view enables models to learn and improve through techniques like reinforcement learning through human feedback (RLHF), fine-tuning, and process recreation. Startups that horizontally integrate their processes gain a competitive advantage, allowing them to replace external solutions and gain more control over the entire process.

The Importance of Integration and Privacy Concerns:

For AI-based companies, integration over various layers of the value chain is crucial to access valuable data and improve performance. OpenAI's decision to develop ChatGPT was driven by the desire to receive direct human feedback from end users. Similarly, startups like Replit, which integrate over the process of turning ideas into software, exemplify the power of forward integration. However, integration also raises privacy concerns and may face resistance from incumbents. Startups that prioritize owning end-to-end processes and centrally storing data for model improvement will have a competitive edge.

The Enigma of AI and Predictions:

AI's predictive capabilities extend beyond scientific explanations. It can make predictions about phenomena that are yet unexplained by science, such as anxiety. By encoding elements of these phenomena within its network, AI provides us with predictions that our rational minds may struggle to comprehend. This raises fascinating questions about the role of intuition and storytelling in facilitating predictions beyond our limited rationality.

Actionable Advice:

  • 1. Embrace simplicity in note-taking: Instead of relying on elaborate systems, focus on capturing ideas that naturally stick in your mind. Allow your natural salience filter to guide you.
  • 2. Explore end-to-end interaction data: If you're involved in AI development, prioritize collecting comprehensive data on the lifecycle of processes. This knowledge can enhance models' abilities to learn, automate, and improve over time.
  • 3. Seek integration opportunities: Consider integrating your offerings over various layers of the value chain. By replacing external solutions and gaining control over the entire process, you can enhance data access and improve overall performance.


In the realm of both note-taking systems and AI knowledge orchestration, simplicity and organic thinking play crucial roles. While note-taking should complement the creative process without becoming the sole focus, AI builders must tackle the challenge of effectively orchestrating knowledge for reasoning models. By embracing simplicity, leveraging end-to-end interaction data, and seeking integration opportunities, individuals and companies can enhance their note-taking practices and achieve remarkable advancements in AI development.

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