The video argues that the most important “programming language” today is not Python, C++, or JavaScript, but English. Large language models (LLMs) are essentially trained on English text, making the quality and structure of English prompts crucial for effective communication with these models. The speaker compares two prompts: one unstructured and lazy, and another well-structured, direct, and clear. The structured prompt consistently yields better results, demonstrating that clarity, conciseness, and organization in English are key to maximizing LLM performance.
The importance of structure in prompts is emphasized, with data showing that well-structured prompts score significantly higher on average than raw, unstructured ones. The speaker highlights that simpler English is more effective than complicated language when interacting with LLMs. Complex vocabulary and convoluted phrasing can confuse models, whereas straightforward and well-organized language helps them understand and respond more accurately. This simplicity and clarity are essential because LLMs are trained on vast amounts of internet data, where clear communication like that found on platforms such as Stack Overflow is common.
The video also discusses the evolving role of system prompts and the “middle layer” in LLMs, which influence how inputs are interpreted and outputs generated. Over time, system prompts have become simpler and more structured, reflecting a better understanding of how to communicate with these models. While this middle layer can enhance general use cases, it also introduces risks, especially when third parties control it. Some applications bypass this layer to avoid unintended interference, underscoring the importance of clear and simple English in both user prompts and system-level instructions.
A key challenge with English as a programming language is its inherent looseness compared to traditional programming languages, which are strict and syntax-driven. While strictness in coding languages ensures precision, English’s flexibility leads to inconsistency in LLM outputs. The speaker notes that even with well-structured prompts, results can vary, and there is no guarantee of exact repetition. However, by applying strategies focused on clarity, structure, and simplicity, users can improve the average quality of responses from LLMs.
Finally, the speaker clarifies that they are not suggesting English majors outperform computer scientists but rather that computer scientists benefit greatly from mastering English, especially in prompt crafting. A strong command of English can improve effectiveness by up to 30% or more when working with LLMs. The video concludes by encouraging viewers to simplify their language, learn grammar and punctuation, and embrace English as the new essential language for programming and interacting with AI systems.
