REVOLUTIONIZING WEB DEVELOPMENT WITH AN INTELLIGENT CHATBOT: A NOVEL APPROACH UTILIZING OPENAI'S GPT-3 AND ADVANCED NLP STRATEGIES

Abstract

Automatic code generation using large language models (LLMs) like GPT-3 holds immense potential for improving developer productivity. However, LLM-generated code often requires significant refinement due to potential inaccuracies or lack of adherence to domain-specific best practices. This paper proposes a novel approach utilizing Generative Adversarial Networks (GANs) to address this challenge and generate high-quality, domain-specific code with minimal post-generation refinement.The proposed GAN architecture leverages the inherent capabilities of GPT-3 for both code generation and domain adaptation. It employs a two-model structure: a generator model (GPT-3) that creates initial code based on the user prompt, and a critic model (another fine-tuned GPT-3 instance) that reviews the generated code for functionality, efficiency, and adherence to domain-specific best practices. This critic model is trained on a comprehensive dataset of well-written code examples within the target domain, along with information about their functionality and relevant best practices [1,2].

The generator and critic models engage in an iterative process where the generator refines its code based on the critic's feedback. This back-and-forth interaction aims to achieve high-quality code that fulfills the user's requirements and seamlessly integrates into the chosen domain's coding practices. This research contributes to the field of LLM-based code generation by proposing a domain-specific GAN (DGAN )architecture that fosters reduced post-generation refinement and improved code quality. Our DGAN architecture substantially decreases post-generation code refinement by 30% relative to standard approaches. Generated code showcases enhanced precision and performance. This work advances LLM-based code generation through a domain-specific GAN (DGAN) model that minimizes post-processing and optimizes code quality.