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. As such, minimizing the series of prompt enhancements 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 absolute 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 underline 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.

Kanchanjot Kaur Phokela

Accenture

India

Samarth Sikand

Accenture Labs

Kapil Singi

Accenture

Kuntal Dey

Accenture Labs, India

Vibhu Saujanya Sharma

Accenture Labs

Vikrant Kaulgud

Accenture Labs, India

India