The AI industry's race for larger context windows is hitting a wall. Models like GPT-4 Turbo and Claude 3 now handle millions of tokens, but real-world deployment reveals a paradox: stuffing more context often degrades performance. This stems from the 'Lost in the Middle' phenomenon, where Transformers struggle to attend to information buried in long prompts, dropping accuracy by 20-30%.

The core issue isn't capacity but curation. Quadratic attention costs mean processing 100,000 tokens requires 10 billion calculations, exploding latency and API bills. A legal tech firm's contract review tool hallucinated critical clauses because the model couldn't focus on key information amidst noise. The solution is treating context as a computational budget, not a storage bin.

Context engineering emerges as the discipline for this new reality. It involves dynamic token budgeting based on query intent, semantic chunking that breaks documents at idea boundaries, and predictive prefetching to reduce latency. The goal shifts from 'fitting more' to 'fitting smarter,' balancing cost, speed, and accuracy through intelligent retrieval and compression strategies.