Efficient context windowing tackles the quadratic cost of attention by limiting the number of tokens the model processes at once while preserving relevant information. Core strategies include fixed‑size sliding windows that shift across long texts, hierarchical attention that first aggregates local token groups before a global pass, and retrieval‑augmented generation which pulls only the most pertinent passages from an external datastore. Token‑compression methods such as pooling, quantization, or low‑rank factorization shrink representations, enabling larger effective windows. Dynamic window sizing adapts the window length based on content density, expanding around dense information and contracting over filler text. Caching previously computed attention results prevents redundant calculations when windows overlap. Hybrid approaches combine these techniques—e.g., a sliding window with a cached global summary—balancing latency, memory, and accuracy. Implementation considerations involve careful token‑to‑semantic alignment, avoiding boundary artifacts, and ensuring that window‑level gradients remain stable during training. Benchmarks on long‑document QA, code completion, and narrative generation show that well‑tuned windowing can achieve near‑full‑context performance with up to 70% fewer FLOPs.