Large language models (LLMs) have seen a significant increase in the size of their context windows, which is the maximum amount of text that an LLM can process at once. This has led to more extensive and complex inputs being handled by LLMs, potentially resulting in more informed and coherent outputs. The larger context windows allow for more documents and data formats to be sent to the LLM with each request, enabling it to process more complex information in a single request. This can have a big impact on tasks that require understanding and combining a lot of information, such as processing entire contracts or research papers at once. Long Context Language Models (LCLMs) are emerging as a promising approach for retrieval augmented generation (RAG), which has been the de facto standard setup for eliciting useful and fact-based responses from LLMs. LCLMs can provide more detailed answers than simple RAG implementations, especially for complex queries that require combining information from multiple sources. The trend towards larger context models opens up the possibility of feeding more data into the LLM when traditional RAG isn't enough, making them useful for tasks like comparing multiple documents or requiring continuous context.