Leveraging AI for Research and Design Before Code Generation
A viral post highlights a highly efficient workflow for AI coding assistants like OpenAI Codex, emphasizing research, technology selection, and design planning before generating code. Shifting the use of LLMs from direct code generation to early-stage planning helps developers avoid architectural mistakes and align project scopes more effectively. The recommended workflow involves using Codex to research similar open-source software (OSS), analyze success and failure cases, select technology stacks, and define the Minimum Viable Product (MVP) scope before implementation.
## BACKGROUND
OpenAI Codex is an AI model descendant of GPT-3 trained on both natural language and billions of lines of public source code. While many developers use it primarily to write code, its capabilities extend to high-level software engineering tasks like system design and technical research.