Smart Prompt Advisor: Multi-Objective Prompt Framework for Consistency and Best Practices

Recent breakthroughs in Large Language Models (LLM), comprised of billions of parameters, have achieved the ability to unveil exceptional insight into a wide range of Natural Language Processing (NLP) tasks. The onus of the performance of these models lies in the sophistication and completeness of t...

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Vydáno v:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] s. 1846 - 1848
Hlavní autoři: Phokela, Kanchanjot Kaur, Sikand, Samarth, Singi, Kapil, Dey, Kuntal, Sharma, Vibhu Saujanya, Kaulgud, Vikrant
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 11.09.2023
Témata:
Artificial Intelligence
Codes
Costs
Deep Learning
LLM
Natural language processing
Ontology
Prompt Engineering
Semantics
Task analysis
Time to market
Visualization
ISSN:2643-1572
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Shrnutí:Recent breakthroughs in Large Language Models (LLM), comprised of billions of parameters, have achieved the ability to unveil exceptional insight into a wide range of Natural Language Processing (NLP) tasks. The onus of the performance of these models lies in the sophistication and completeness of the input prompt. Minimizing the enhancement cycles of prompt with improvised keywords becomes critically important as it directly affects the time to market and cost of the developing solution. However, this process inevitably has a trade-off between the learning curve/proficiency of the user and completeness of the prompt, as generating such a solutions is an incremental process. In this paper, we have designed a novel solution and implemented it in the form of a plugin for Visual Studio Code IDE, which can optimize this trade-off, by learning the underlying prompt intent to enhance with keywords. This will tend to align with developers' collection of semantics while developing a secure code, ensuring parameter and local variable names, return expressions, simple pre and post-conditions. and basic control and data flow are met.
ISSN:2643-1572
DOI:10.1109/ASE56229.2023.00019