In 2023, everyone became a computer programmer.
Quoting my friend José Eduardo Venson: "2023, the year everyone became a programmer," which I read in this very interesting post. This idea has been coming to me in recent days, but in a nebulous way. Some thoughts came to mind.
Large Language Models (LLMs) like OpenAI's GPT-4 and Google's Bard have an incredible ability to understand instructions in natural language and generate code in various programming languages. This opens doors for individuals without programming knowledge to write useful software, as they can describe what they want the software to do in natural language, and the LLM can generate the corresponding code.
There are, however, some significant limitations to this type of usage. First, while LLMs can generate code from natural language descriptions, the quality of the generated code heavily depends on the quality of the description. A vague or ambiguous description can lead to code that doesn't work as intended.
Second, LLMs do not have the ability to understand or model context beyond what was provided in text. That is, they do not have long-term memory or the ability to maintain an internal world state. This means if you ask the LLM to generate code for a part of a larger program, it will not be able to understand the broader context of that program unless that context is explicitly provided.
Third, while LLMs are good at generating code that seems plausible at first glance, they do not guarantee the correctness of the code. In other words, the generated code may have errors, and these errors can be difficult to detect without a deep understanding of programming.
The more detailed and complex the application to be built, the greater the need to detail the request to the LLM, a process also known as "prompt engineering". The challenge here is that effectively detailing this request requires substantial knowledge of logic and algorithms, which are some of the foundations of programming.
For example, to build a hotel booking system, you would not only need to describe high-level functions (like searching for available rooms, booking a room, canceling a booking), but also the details of how these functions interact with each other and the hotel's database. This is a complex process that requires a deep understanding of the problem and programming techniques.
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Therefore, while LLMs can act as translators of pseudocode in natural language to programming code of a specific language, the effectiveness of this translation largely depends on the user's understanding of the underlying logic and algorithms. To effectively use ChatGPT for programming, people who don't know a programming language will still need to understand the reasoning behind the code.
This text was written with the help of GPT, although the ideas were provided by me.
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1 年Great insights, Felipe! Your analysis of the limitations and challenges of using LLMs like GPT for programming is spot on. It's true that while these models can generate code from natural language, the quality and correctness heavily depend on clear and detailed descriptions, as well as the user's understanding of programming logic. It's an exciting area of development, but it still requires human expertise to ensure effective translation from pseudocode to functional code. Well done!