LongLLMLingua is presented as a solution to enhance Retrieval-Augmented Generation (RAG) by addressing issues like performance drops, high costs, and context window limitations through advanced prompt compression techniques. By implementing Re-ranking, Fine-Grained Prompt Compression, and Subsequence Recovery, LongLLMLingua improves the accuracy of RAG scenarios by 21.4% while reducing token usage by 75%, which translates to significant cost savings in long-context situations. The approach emphasizes the importance of reducing noise in prompts and repositioning key information to improve LLM performance. It introduces a Question-aware Coarse-Grained Prompt Compression method that leverages perplexity to evaluate context relevance and mitigate hallucinations. Experiments demonstrate that LongLLMLingua outperforms existing retrieval and compression-based methods by preserving key information and speeding up inference processes, making it highly effective for real-world applications like multi-document QA and long-context benchmarks. The technique is now integrated into the LlamaIndex framework as a NodePostprocessor, offering users an efficient tool for managing RAG tasks.