How does OpenAI Codex convert natural language into efficient and secure code?

[ghost]

I’ve been exploring OpenAI Codex and am curious about the inner mechanism that allows it to translate human language into optimized, executable code.

• How does Codex interpret complex natural language instructions?
• Does it maintain context like variable scope, dependencies, and error handling during generation?
• How does it ensure that the generated code remains secure and efficient, especially when handling user inputs or API calls?

I’d appreciate a detailed explanation of how Codex handles these challenges internally and maintains both accuracy and performance.

[mmaunder]

I would strongly encourage the poster to dive into course.fast.ai which is completely free and will give you a foundational understanding of AI models, starting with the fundamentals and taking you all the way up to large language models, which is what provides the cognitive capability behind codex.