Context Window Explained: What Happens When It Fills Up | LLM Interview Questions

"What is the context window, and why is it limited?" sounds like an easy interview question. Most candidates answer "it's how much the model remembers." That answer quietly costs them the senior role. In this video we break it down from zero, using one simple picture: a desk. The model only sees what's on the desk, the desk has a fixed size, and everything you put on it costs money and attention. You'll learn: ✅ What the context window really is (and why "memory" is the wrong word) ✅ Why it's limited: attention cost, KV cache memory, and training length ✅ What happens as it fills up: cost, latency, "lost in the middle", forgotten instructions ✅ Why a 10-turn chat sends 27,500 tokens, not 5,000 ✅ How seniors manage it: summarizing, sliding windows, RAG, prompt caching ✅ The follow-up question interviewers love, and a 60-second answer you can use word for word Interview questions answered in this video: • What is the context window in LLMs, and why does it matter? • Why is the context window limited in LLMs? • What happens as you fill the context window? • What is a token, a context window, and why do they matter to strategy? • What is the role of the context window during LLM inference? 📌 Three things to remember: 1. The desk, not the brain. 2. Everything on the desk costs money and attention. 3. A bigger desk is not a better reader. ⏱️ Chapters The question most candidates get wrong The desk analogy What shares the context window Why the window is limited Prefill vs decode: how the model uses it What happens as it fills up Strategy: managing the token budget Myth: bigger window = better memory The interviewer's follow-up question Junior vs senior answer Recap and quick quiz ▶️ Full LLM Foundations playlist:    • What Is a Token in LLMs? (AI Interview Que...   If this helped, subscribe. A new AI engineering interview question every week. #LLM #AIInterview #ContextWindow #MachineLearning #GenerativeAI #PromptEngineering #RAG #AIEngineering