Retrieval-Augmented Generation (RAG) is a powerful technique that empowers large language models (LLMs) by connecting them to external knowledge sources. However, current RAG systems grapple with high costs and efficiency issues, especially when dealing with long, information-rich texts. A new approach called FlexRAG aims to address these challenges. FlexRAG's innovation lies in its ability to compress retrieved contexts into compact embeddings before feeding them to the LLM, making the process more efficient. It also optimizes these compressed embeddings for specific tasks, ensuring that the LLM gets the most relevant information without being bogged down by excessive data. Unlike previous methods that compress all contexts uniformly, FlexRAG offers flexibility by allowing different compression ratios based on the importance of the information. More critical information is compressed less, preserving its richness, while less vital parts are compressed more aggressively, saving processing power. The system learns to prioritize what to keep and what to discard using a combination of task-agnostic pre-training on a large text corpus and task-specific fine-tuning on datasets for tasks like question answering. This two-stage approach builds a strong foundation and refines it to excel at specific tasks. Early tests on various question-answering datasets show promising results. FlexRAG not only improves the efficiency and cost-effectiveness of RAG systems but also enhances the quality of generated answers, demonstrating a balance of efficiency and effectiveness. Its flexible compression scheme and task-specific tuning approach contribute to better utilization of available resources. The development of FlexRAG represents a significant step towards practical, real-world applications of RAG, offering a more efficient and powerful way to harness the potential of LLMs for complex knowledge-intensive tasks. Future exploration may include adapting FlexRAG to other LLM architectures and applying it to a wider range of tasks beyond question answering, potentially opening up more exciting possibilities in the field.