Prompt Engineering is Tacit Knowledge Exposed

Prompt engineering is the way humans interact with large language models. It involves creating inputs that guide these models to produce specific outputs. This process depends on the prompt engineer's personal insights and experiences, known as tacit knowledge. This essay examines how prompt engineering reveals the engineer's tacit knowledge, based on traditional knowledge management theory.

What is tacit knowledge?

Knowledge management theory separates knowledge into explicit and tacit forms. Explicit knowledge is formal and easy to communicate, like manuals or databases. Tacit knowledge is personal and hard to formalize. It includes skills and experiences. Michael Polanyi stated, "We know more than we can tell." This shows the difficulty of expressing tacit knowledge.

Nonaka and Takeuchi's SECI model describes how knowledge transforms between tacit and explicit forms. The model includes Socialization, Externalization, Combination, and Internalization. It emphasizes how these forms of knowledge interact. This interaction supports learning and innovation in organizations.

How are prompts tacit knowledge?

Prompt engineering uses tacit knowledge. Consider an individual proficient in Python but unfamiliar with D3, a JavaScript library for data visualization. They need to create a complex visualization using D3. Their tacit knowledge in programming logic and computing helps them prompt a generative AI model to produce the correct D3 code. They understand programming structures, algorithms, and data manipulation. This expertise allows them to guide the model effectively, even without direct knowledge of D3 syntax.

By asking the right questions and refining prompts, they bridge the gap between their Python experience and the D3 code they need. Their tacit knowledge shapes the prompts they create. It enables them to communicate their programming intentions to the large language model. This process reveals their unspoken understanding of programming concepts.

What are the benefits of exposing tacit knowledge?

The act of prompt engineering exposes tacit knowledge. Through initial prompts and iterations with the generative AI model, the prompt engineer transfers their unexpressed knowledge directly into the prompts. They articulate their understanding of programming logic by instructing the model to produce specific results. Each prompt reflects their internal thought process. As they refine their prompts, they make their implicit knowledge explicit.

This externalization aligns with the SECI model's concept of turning tacit knowledge into an explicit form. By interacting with the large language model, the engineer documents their thought process. The prompts become artifacts of their tacit knowledge. This not only helps them achieve their immediate goal but also creates a record that can aid others.

How do we understand this using the SECI model?

The SECI model's processes appear in prompt engineering. Socialization happens when engineers share experiences while working together. Externalization occurs as they write and refine prompts, making tacit knowledge explicit. Combination involves gathering these prompts to build resources, enhancing the organization's explicit knowledge. Internalization happens when others study these prompts, turning explicit knowledge back into tacit understanding that guides their work.

In the example, the engineer's prompts and interactions with the generative AI can be documented. Others can learn from these prompts to understand how to bridge gaps between different programming languages. This shared knowledge supports collective learning and innovation.

What are the implications?

Understanding prompt engineering through knowledge management shows that organizations need to support sharing and using tacit knowledge. Providing platforms for employees to share prompts and insights helps. Encouraging the capture and sharing of these prompts turns tacit knowledge into an explicit form while also furthering the work of the individual and the goals of the firm. This prompt engineering library becomes a valuable knowledge management resource. It helps others leverage generative AI models more effectively. It conveys tacit knowledge in a digestible, actionable form, and it fosters an environment where tacit knowledge is continuously transformed and shared in the organization.