{
  "id": 12576657,
  "title": "Knowing the Words Isn't Knowing the Language",
  "url": "https://urgent.news/2026/10/07/knowing-the-words-isnt-knowing-the-language",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-10-07T07:42:51.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/testingil/knowing-the-words-isnt-knowing-the-language-4n2o"
  },
  "original_language": "en",
  "account": "Streaks on language learning apps can be impressive, but they don't equate to fluency. Arriving in a foreign country and attempting to order food using only a limited vocabulary can be a humbling experience. Knowing the words is not the same as knowing the language. This is a lesson that applies to coding as well. At its core, code is made up of words that computers can understand. If we could communicate with computers using natural language, it would be ideal. However, the challenge lies not in the words themselves, but in how the computer interprets them. The solution is code, an abstraction that sits above the raw ones and zeros that make up computer instructions. While knowing a few key terms is not enough to achieve desired outcomes, the more proficient you are in the language, the better the results will be. The process of turning human requirements into working code involves multiple stages, each requiring translation. From understanding what the customer wants, to designing the solution, writing the code, building it, and verifying the final product, each step is a translation that can introduce errors. These errors can be mitigated through reviews, testing, and other quality assurance measures. However, as we delegate more and more tasks to AI, we lose control over the translation process. The genie may produce code that works, but it may not align with our expectations. This can lead to security vulnerabilities, performance issues, and other unintended consequences. Reviewing the code produced by AI is crucial, just as we would review any other aspect of a project. Breaking down the code into smaller, more manageable chunks can make the review process more effective. Refactoring and improving the code as we go along is also essential. Ultimately, while AI can help write code, it is up to us to review, understand, and improve it. The onus is on us to ensure that the code meets our expectations and works as intended.",
  "summary": "You've got a 200-day Duolingo streak. The owl is proud of you. Then you land in Rome, sit down in a trattoria, and order. Every word is owl-approved. The grammar too. You think. The waiter nods, smiles, and brings you something you don't really want. Is beef cheeks a thing? Knowing the words isn't knowing the language. Even with a 200-day streak. Code is made of words. Words that computers…",
  "key_points": [
    "Knowing words doesn't equate to language fluency",
    "Code abstraction solves human-computer language gap",
    "AI code review crucial for quality assurance"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}