WorkDifferentWithAI.com Academic Paper Alert!
Written by Douglas C. Schmidt, Jesse Spencer-Smith, Quchen Fu, Jules White
Article Section: Applications; Content Workflow and Management Applications
Publication Date: Pre-publication copy found Nov 9 2023
SEO Description: This paper proposes codifying prompt patterns to develop the discipline of prompt engineering for reliable AI systems.
AI-Generated Paper Summary
Here is my analysis of the novelty and potential commercial applications of this academic paper:
This paper makes several novel contributions to the emerging field of “prompt engineering” for large language models (LLMs) like ChatGPT:
- It advocates for a more systematic, disciplined approach to interacting with LLMs beyond isolated, ad hoc examples. The authors argue that codifying “prompt patterns” provides a foundation for reliable prompt engineering.
- The paper introduces and demonstrates a set of prompt patterns spanning requirements gathering, question refinement, error handling, and more. While prompt examples exist, formally defining patterns is a novel idea.
- The authors propose prompt engineering as a new software engineering paradigm that complements traditional coding. Viewing prompts as a “natural language architecture” for LLMs is a unique perspective.
- Specializing prompt patterns through abstraction, similar to object-oriented subclassing, is presented as a novel way to enhance reuse and accelerate LLM interactions.
- The idea of an LLM assisting in refining user prompts via patterns like Question Refinement is an original concept not seen in prior work.
Overall, the paper makes conceptual and practical contributions to establishing prompt engineering as a rigorous discipline built on canonical patterns.
Several commercial opportunities emerge from the concepts presented:
- A prompt pattern library and toolkit could be productized for commercial use. APIs and GUI tools could leverage the patterns.
- Consulting services focused on prompt engineering, applying patterns to client needs. Particularly useful for non-CS domains.
- Proprietary sets of tuned, optimized patterns tailored for specific industries like healthcare or manufacturing.
- Services that manage and track prompt quality through rigorous prompt testing and validation based on patterns.
- Prompt debugging tools that leverage patterns like Reflection and Question Refinement to improve prompts.
- Commercial LLM services differentiated by more advanced prompt pattern capabilities compared to basic models.
- Prompt optimization techniques and tools specialized to the nuances of commercial applications.
- Vertical applications of prompt engineering in areas like requirements elicitation, QA testing, report generation, simulations, etc.
In summary, the conceptual foundations introduced in this paper could support a range of commercial products, services, and business models centered around prompt engineering. The patterns provide a strong applied basis for commercialization.
prompt engineering, software engineering, prompt patterns, natural language, question refinement