Logo des Repositoriums

Auflistung nach Schlagwort "prompt engineering"

1 - 2 von 2
  • Konferenzbeitrag
    Engineering A Reliable Prompt For Generating Unit Tests - Prompt engineering for QA & QA for prompt engineering
    (Softwaretechnik-Trends Band 43, Heft 3, 2023) Faragó, David
    This paper demonstrates Prompt Engineering (PE) on a running example: generating unit test cases for a given function. By iter atively adding further prompt patterns and measuring the robustness, correctness, and comprehensiveness of the AI’s output, multiple prompt patterns and their purpose and strength are investigated. We conclude that high robustness, correctness, and comprehensiveness is hard to achieve, and many prompt patterns (single prompt as well as patterns that span over a conversation) are necessary. More generally, quality assurance is a dominant part of PE and closely intertwined with the development part of PE. Thus traditional testing processes and stages do not adequately apply to QA for PE, and we suggest a PE process that covers the development and quality assurance of prompts as alternative.
  • Konferenzbeitrag
    Large Language Models are Pattern Matchers: Editing Semi-Structured and Structured Documents with ChatGPT
    (AKWI Jahrestagung 2024, 2024) Weber, Irene
    Abstract: Large Language Models (LLMs) offer numerous applications, the full extent of which is not yet understood. This paper investigates if LLMs can be applied for editing structured and semi-structured documents with minimal effort. Using a qualitative research approach, we conduct two case studies with ChatGPT and thoroughly analyze the results. Our experiments indicate that LLMs can effectively edit structured and semi-structured documents when provided with basic, straightforward prompts. ChatGPT demonstrates a strong ability to recognize and process the structure of annotated documents. This suggests that explicitly structuring tasks and data in prompts might enhance an LLM’s ability to understand and solve tasks. Furthermore, the experiments also reveal impressive pattern matching skills in ChatGPT. This observation deserves further investigation, as it may contribute to understanding the processes leading to hallucinations in LLMs.