Netflix's Graph Search platform is evolving to incorporate natural language search using AI, addressing limitations of its structured query language. Natural language search simplifies data retrieval, improving user experience across various applications. The core challenge involves translating user queries into valid Graph Search Filter statements. This process uses Large Language Models (LLMs) to generate queries, incorporating context engineering for accuracy. Context engineering leverages GraphQL schema information and controlled vocabularies to inform the LLM. Retrieval-Augmented Generation (RAG) is employed to manage context, focusing on relevant fields and controlled vocabulary values. Field RAG and Controlled Vocabularies RAG enhance precision by matching user intent to index elements. The process involves vector search and deduplication for efficient context management. The system provides instructions to the LLM to generate syntactically, semantically, and pragmatically correct filters. The effectiveness of the solution requires careful parameter tuning for RAG components. The described work paves the potential for building a RAG system on top of Graph Search. Future articles will detail the implementation, performance evaluation, and platform evolution.