Large language models (LLMs) are revolutionizing how we code, translating human language into functional programs. However, these powerful AI tools sometimes stumble over niche technical terms, leading to inaccurate code. Imagine an LLM trying to understand the concept of "even digits." While it might grasp "even numbers," the specific meaning of even digits (0, 2, 4, 6, and 8) might be lost in translation. This is where the exciting new research on Self-Explained Keywords (SEK) comes in. Researchers have found that by explicitly explaining and ranking keywords by their frequency, LLMs can generate dramatically more accurate code. It's like giving the LLM a cheat sheet with definitions and highlighting which terms are most important. This approach mimics how human developers work, jotting down and clarifying key requirements before diving into code. The results are impressive. Across several challenging coding benchmarks, LLMs equipped with SEK showed significant improvements, sometimes boosting accuracy by nearly 10%. This simple technique enables LLMs to bridge the gap between common terms and specialized vocabulary, generating more precise code that reflects the programmer's intent. The implication is huge—we're not just making LLMs better coders; we're making them better problem-solvers. By guiding their attention toward core concepts, we unlock their true potential, paving the way for even more sophisticated AI-powered coding tools. While SEK requires calling the LLM twice – once to identify keywords, and another time to generate the code – the payoff in accuracy makes it a worthwhile trade-off. Future research could explore combining these two steps for even faster, more efficient code generation. Furthermore, refining how LLMs identify and rank keywords, especially when dealing with complex or ambiguous concepts, remains a crucial area of exploration. As LLMs continue to evolve, techniques like SEK will be essential to improving their reliability and efficiency, ultimately transforming how we build software.