In this project, I have used two powerful LLMs—OpenAI’s GPT (through ChatGPT) and Anthropic’s Claude—to generate fully functional CLI scaffolding code. By crafting a precise prompt, i instructed the model to create an application that asks users a series of questions, then generates boilerplate files and directories for various languages (such as Python, JavaScript, Go, and Rust), containerization setups, CI/CD pipelines, and even Terraform scripts for cloud deployment.
The code you see was not written by hand. Instead, i iteratively refined the prompt to guide the LLMs toward the desired structure and features. Each version of the prompt contained instructions about essential files to generate, testing setups, cloud deployment configurations, and more. Within moments, the LLM returned fully fledged project scaffolds ready to be saved and executed. By harnessing LLMs in this way, developers can rapidly bootstrap complex projects, saving time and enabling more focus on unique business logic and innovation rather than repetitive setup tasks.
This also takes in additional requirements - where we can give additional prompts to shape the generated code. We have implemented the code with both OpenAI API and Claude API
GitHub - karthiksuk/interactive-code-generator: This is an interactive code generator with the help of llm
This is an interactive code generator with the help of llm - karthiksuk/interactive-code-generator
