"Knowledge Orchestration and Early Work in the Age of AI"


Hatched by Glasp

Sep 16, 2023

4 min read


"Knowledge Orchestration and Early Work in the Age of AI"


In the age of AI, there are two crucial components to intelligence: reasoning and knowledge. While GPT-4 excels in reasoning abilities, its knowledge of the world remains limited. This limitation poses a challenge for builders in AI, as they need to provide the right knowledge at the right time to enhance GPT-4's performance. This problem, known as knowledge orchestration, is currently one of the biggest unsolved problems in AI. In this article, we will explore the concept of knowledge orchestration, the value of end-to-end interaction data, the importance of horizontal integration, and the role of AI in predicting and explaining the unexplainable.

Knowledge Orchestration: Improving AI Performance:

Knowledge orchestration refers to the process of storing, indexing, and retrieving knowledge required for performing language tasks effectively. Builders are continuously working on improving this aspect by developing larger context window sizes. GPT-4's 32,000 token context window, for example, is a significant improvement over previous models. Additionally, developers are creating tools and infrastructure, such as LlamaIndex, Langchain, and vector database providers like Pinecone, to facilitate knowledge chunking, storage, and retrieval. These advancements in knowledge orchestration are essential for enhancing AI's reasoning capabilities.

End-to-End Interaction Data: Enhancing AI Learning:

One type of knowledge that holds immense value is end-to-end interaction data about the lifecycle of any process. Whether it's a company's internal process or a process enabled for customers, having access to the entire journey, from the beginning to the end, allows for better steering of models. With techniques like reinforcement learning through human feedback (RLHF), fine-tuning, and prompting, models can learn to recreate and improve these processes automatically. Startups that horizontally integrate over a process have a significant advantage in this AI-driven world, as they can replace external solutions with their own, enabling them to have complete visibility and integration.

The Role of Early Work: Overcoming Challenges:

One of the primary reasons people fail to achieve remarkable results is the fear of creating something unimpressive. The lack of experience in dealing with the initial versions of ambitious projects leaves individuals unsure of how to respond. Surrounding oneself with the right people who believe in the vision and creating a supportive environment can help overcome the resistance faced when starting something new. The internal doubt one experiences is often more powerful than the external skepticism of others. Embracing the early, imperfect stages of ambitious projects is crucial to progress.

AI's Ability to Predict and Explain the Unexplainable:

AI has the power to make predictions about aspects of the world that science cannot yet fully explain. For example, if AI becomes an accurate predictor of anxiety, it suggests that it has encoded at least part of the explanation for anxiety within its neural network. This leads to an intriguing possibility where intuition and storytelling, rather than rational thinking, might be the most effective ways our minds predict and explain things beyond our comprehension. AI's ability to delve into the unexplainable opens up new avenues for understanding and discovery.

Actionable Advice:

  • 1. Embrace the early stages of ambitious projects: Overcome the fear of creating something unimpressive and recognize that progress requires experimentation and iteration. Surround yourself with supportive individuals who believe in your vision.
  • 2. Harness end-to-end interaction data: If you're involved in a process, strive to gain a comprehensive understanding of its entire lifecycle. This knowledge will enable you to steer AI models effectively and continuously improve the process over time.
  • 3. Seek horizontal integration: In an AI-driven world, companies that horizontally integrate over a process by replacing external solutions with their own gain a competitive advantage. By owning the end-to-end process and centrally storing data, startups can optimize their models and enhance overall performance.


In the realm of AI, knowledge orchestration and early work play crucial roles in improving performance and achieving remarkable results. By understanding the value of end-to-end interaction data, embracing the imperfections of early stages, and seeking horizontal integration, builders can navigate the challenges and harness the potential of AI. Furthermore, as AI continues to predict and explain the unexplainable, it challenges our conventional understanding of rationality, paving the way for new insights and discoveries.

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