As language models continue to revolutionize the field of natural language processing, businesses are looking to harness their power for internal applications and processes. However, it's crucial to understand the limitations of pre-trained language models, particularly their lack of access to up-to-date information and tendency to produce inaccurate responses. In this post, we explore retrieval-augmented language modeling (RALM) as a promising solution to overcome these limitations. By embedding factual content directly into the user's prompt, RALM enables language models to generate more accurate and relevant responses, while also allowing organizations to leverage their internal data sources and build flexible retrieval systems.