Context-Augmented Generation (CAG) is emerging as a powerful alternative to the traditional Retrieval-Augmented Generation (RAG) for extending the capabilities of large language models (LLMs). While RAG relies on retrieving relevant data chunks from a knowledge base, CAG simplifies the process by loading entire documents into the LLM's context window, a method made feasible by the dramatic increase in context lengths from 4K tokens to 1-2 million tokens. This approach eliminates complex retrieval mechanisms and improves retrieval accuracy, particularly when documents fit within the context limits. CAG is best suited for scenarios where document sets are less than 1-2 million tokens, facilitating high accuracy with minimal implementation effort. As the cost of token usage becomes more affordable, tools like Helicone can help monitor implementation costs and optimize performance. Although CAG is not a complete replacement for RAG, it offers a compelling solution for specific applications, especially as LLM context windows continue to expand.