REVIEW 5 cited by
Exploring Prompt Engineering Practices in the Enterprise
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Interaction with Large Language Models (LLMs) is primarily carried out via prompting. A prompt is a natural language instruction designed to elicit certain behaviour or output from a model. In theory, natural language prompts enable non-experts to interact with and leverage LLMs. However, for complex tasks and tasks with specific requirements, prompt design is not trivial. Creating effective prompts requires skill and knowledge, as well as significant iteration in order to determine model behavior, and guide the model to accomplish a particular goal. We hypothesize that the way in which users iterate on their prompts can provide insight into how they think prompting and models work, as well as the kinds of support needed for more efficient prompt engineering. To better understand prompt engineering practices, we analyzed sessions of prompt editing behavior, categorizing the parts of prompts users iterated on and the types of changes they made. We discuss design implications and future directions based on these prompt engineering practices.
Forward citations
Cited by 5 Pith papers
-
Strategy-Induct: Task-Level Strategy Induction for Instruction Generation
Strategy-Induct induces task-level instructions from question-only examples by generating reasoning strategies first, then using those pairs to create a guiding instruction.
-
Alignment has a Fantasia Problem
AI alignment must move beyond assuming users have fully formed goals and instead provide active cognitive support to help form and refine intent over time.
-
Prompt Orchestration Markup Language
POML is a markup language that structures LLM prompts, embeds multimodal data, and decouples formatting via stylesheets, with case studies showing strong prompt format sensitivity.
-
Less Back-and-Forth: A Comparative Study of Structured Prompting
Checklist-improved prompts achieve the highest mean rubric score (7.50/8) and best quality-effort tradeoff compared to raw prompts (5.67) and clarifying-question prompts (6.67) across four task types and three LLMs.
-
Context-Mediated Domain Adaptation in Multi-Agent Sensemaking Systems
Context-mediated domain adaptation treats user modifications to AI artifacts as implicit domain specifications that reshape LLM-powered multi-agent reasoning, demonstrated via the Seedentia system which extracted 46 d...
Discussion (0). Sign in to comment.