How does integrating prompt engineering within a functional-oriented programming language impact the quality and efficiency of AI-generated code compared to using natural language prompts?
12-19-2024
Algorithms are difficult to explain in natural language.
So, why a functional-oriented language? The answer lies in modularity. In software engineering, breaking down problems into smaller, more manageable sub-problems is often the ideal approach. Functional-oriented programming or FOP languages are designed around modularity and composition, relying on functions to decompose complex tasks into smaller, reusable components.
This is where AI comes in. Prompt engineering - the process of designing prompts to effectively communicate with AI models—shares a similar philosophy. Over the past year of using GPT, I've learned that asking an AI model to perform a highly complex task in one go often yields subpar results. Instead, breaking the problem into simpler steps and scaling up tends to produce better outcomes.
This inspired the idea of combining the principles of FOP with prompt engineering. What if we could apply the same philosophy of breaking problems into sub-problems and integrate it directly into a programming language? Imagine a language where prompts could be embedded inside functions to provide additional context and guidance for what the function should accomplish. These prompts would act as keywords, enabling seamless communication with an AI-powered compiler that generates code.
By leveraging large language models for code generation, we can create programs in any language supported by the model, whether it is Solidity for blockchain, C for low-level programming, or Java for object-oriented programming.