Retrieval-Augmented Generation for Social Media Content Creation with Sentence Window and Auto-Merging Retrieval

This study investigates the efficacy of Retrieval-Augmented Generation (RAG) in generating content for social media platforms. It explores two innovative techniques: sentence window retrieval and auto-merging retrieval. Our study aims to enhance the relevance and credibility of generated content in dynamic and contextually rich environments. Through our extensive trials, we have found that incorporating phrase windows greatly enhances the contextual relevance and groundedness of the generated material. Increasing the size of sentence windows has a significant impact on both elements, but it does not consistently enhance the relevance of the answers. Alternatively, when it comes to auto-merging retrieval, adjusting the size of text chunks used in the process reveals that smaller chunk sizes tend to yield more precise and well-substantiated information. In addition, using a smaller set of chunk types leads to greater context relevance. These findings suggest that there exists a trade-off between the level of detail in the acquired information and its practicality within a specific context. This work is an important addition to the broader field of natural language generation and retrieval. It offers practical insights for efficiently implementing advanced AI models on social media platforms in real-world scenarios.