From a recent convo w/ 4o, added some highlights for the good parts:
You’re onto something really intriguing! Drawing a connection between “prompt engineering” and cognitive psychology, particularly through the lens of “information retrieval,” is a rich area for exploration. Let’s dive into how this might work from a cognitive and psychological perspective, especially as it relates to library and information sciences.
1. Cognitive Aspects of Prompt Engineering:
In cognitive psychology, human thought processes are often shaped by how information is framed and retrieved. When we think of prompt engineering as a cognitive task, it could be seen as a form of problem-solving and decision-making that involves formulating the right query (prompt) to retrieve the desired information (output from the AI).
• Mental Models: In order to craft effective prompts, a user must have a mental model of how the AI interprets language. This involves an understanding of the AI’s strengths, limitations, and patterns of behavior, similar to how people develop mental models of search engines or databases when conducting information retrieval tasks.
• Memory and Retrieval Cues: When designing prompts, users draw upon their own knowledge and memory to provide cues that will help the AI retrieve relevant information. This is analogous to how human memory retrieval works—using cues or “prompts” that are most likely to trigger the recall of specific memories or knowledge.
• Language Framing: In cognitive psychology, how we frame a question or problem significantly impacts the kind of solutions we get. This is similar to prompt engineering, where the framing of a prompt affects the model’s interpretation and the output it generates. Cognitive biases, such as anchoring or priming, may also play a role in how users structure their prompts.
2. Information Retrieval in Library Sciences:
In library and information sciences, information retrieval (IR) is concerned with finding information that meets a user’s needs from a vast database or collection. This is similar to the function of large language models (LLMs), like GPT, where the goal is to retrieve or generate information in response to a query (prompt).
• Query Design: Just as a librarian or user must design effective search queries to retrieve relevant documents from a database, prompt engineers must design effective prompts to retrieve relevant information from an AI. Both processes rely on an understanding of how the system indexes and retrieves data.
• Precision and Recall: These are two key concepts in information retrieval. Precision refers to the accuracy of the retrieved information (i.e., how much of what is retrieved is relevant), and recall refers to how much of the relevant information is retrieved. In prompt engineering, one could argue that the goal is to balance precision (getting the correct and specific answer) and recall (getting a comprehensive enough answer).
• Relevance Feedback: In traditional IR, users often refine their searches based on the relevance of the initial results. Similarly, in prompt engineering, users iteratively refine their prompts to get better outputs from the AI, based on the relevance of the initial responses.
3. The Intersection of Cognitive and Information Retrieval Perspectives:
The process of refining prompts to interact with AI involves a combination of cognitive strategies (how we think about language and information retrieval) and information science principles (how systems store and retrieve information).
• Cognitive Load: The user’s ability to generate effective prompts may depend on their cognitive load—how much mental effort is required to process and generate a prompt. Higher cognitive load may reduce the effectiveness of the prompts, just as a poorly constructed query in information retrieval can lead to poor results.
• Search Behavior and Query Reformulation: In library sciences, people tend to engage in query reformulation when their initial search doesn’t yield the desired results. Similarly, users refine their prompts based on the output they receive from the AI, akin to how people adjust their search terms to improve the relevance of the results in a database.
• Context and Intent: Both in cognitive psychology and information retrieval, understanding the user’s intent and context is crucial. A well-engineered prompt, like a well-crafted search query, needs to take the context and intent behind the question into account in order to guide the AI or database toward the most relevant response.
4. Future Speculation: “Prompt Material Sciences”
If we take this a step further into speculative territory, “prompt material sciences” could involve studying how different “materials” (language structures, question framing, and psychological triggers) influence the “strength” or “flexibility” of prompts. For instance:
• Linguistic “Materials”: Different linguistic structures might act as “materials” that affect how effectively a prompt functions. For example, using more concrete versus abstract language could yield different types of responses from an AI.
• Psychological Principles: Research into how different psychological principles (e.g., framing effects, priming, schema activation) influence the effectiveness of prompts could be a key aspect of this field. It might explore how prompts that align with certain cognitive patterns are more successful at extracting accurate or creative information from AI systems.
• Cognitive Bias and AI Interaction: There could be a study of how cognitive biases impact prompt formulation and interaction with AI—similar to how biases affect search behavior in information retrieval systems. For example, confirmation bias might lead users to structure prompts in a way that inadvertently limits the scope of responses.
Conclusion:
By blending cognitive psychology with the principles of information retrieval from library sciences, prompt engineering can be seen as a deeply interdisciplinary practice. It involves understanding not only how language models work but also how humans think, retrieve, and frame information.
This conceptual “prompt material sciences” could focus on the interplay between language (as the “material”) and cognitive processes (the psychology behind prompt creation), aiming to optimize information retrieval from AI systems. The comparison to information retrieval makes perfect sense, as both processes seek to extract relevant information based on well-crafted queries.
