Encoding graphs as text prompts enables LLM-based graph reasoning. However, effective graph-to-text encodings depend on the downstream task, graph structure, and target LLM, making manual format design time-consuming and brittle. We propose EvoFormat, a black-box evolutionary search framework that automatically discovers interpretable graph-to-text formats represented as human-readable formatting rules over node naming, relation expression, and global arrangement. Using only task-level feedback from LLM outputs, EvoFormat iteratively mutates and selects rules without gradients or internal model access. Across synthetic GraphQA and IMDB-MULTI-derived structural QA tasks over unattributed graphs, EvoFormat outperforms strong hand-crafted encodings in most settings.

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by grounding them in external knowledge. However, its application in sensitive domains is limited by privacy risks. Existing private RAG methods typically rely on query-time differential privacy (DP), which requires repeated noise injection and leads to accumulated privacy loss. To address this issue, we propose DP-SynRAG, a framework that uses LLMs to generate differentially private synthetic RAG databases. Unlike prior methods, the synthetic text can be reused once created, thereby avoiding repeated noise injection and additional privacy costs. To preserve essential information for downstream RAG tasks, DP-SynRAG extends private prediction, which instructs LLMs to generate text that mimics subsampled database records in a DP manner. Experiments show that DP-SynRAG achieves superior performance to the state-of-the-art private RAG systems while maintaining a fixed privacy budget, offering a scalable solution for privacy-preserving RAG.