RoPE Scaling is a technique used to improve the context lengths of Large Language Models (LLMs) by adjusting Rotary Position Embedding (RoPE) parameters. This approach enables LLMs to handle longer sequences of text than those seen during training, thereby improving their performance on tasks involving long text generation or understanding. By fine-tuning with adjusted RoPE parameters, LLMs can maintain low perplexity and high accuracy even as the context length increases, which is critical for real-world applications such as document summarization, legal text analysis, and book generation. The technique involves identifying the baseline, adjusting the rotary base value, fine-tuning, evaluating, and iteratively refining to optimize the model's performance on long-context tasks. RoPE Scaling enhances extrapolation capabilities, maintains performance consistency, and broadens the applicability of LLMs, making it an essential method for building more powerful and efficient AI systems.