Retrieval-augmented generation (RAG) effectively combines advanced language models with information retrieval techniques, enabling context-aware and accurate data access. While RAG often focuses on unstructured data, integrating structured data using graph databases like Neo4j can enhance accuracy, despite the challenges of language interpretation and schema-specific nuances. The text2cypher approach translates natural language queries into Cypher database queries but faces accuracy challenges, as showcased in benchmark comparisons using metrics like GoogleBLEU and ExactMatch. Efforts to improve text2cypher accuracy include implementing agentic strategies with LlamaIndex Workflows, employing multi-step approaches, retries, and evaluation phases to refine query generation and execution. These strategies aim to enhance the resilience and accuracy of AI applications, although challenges persist, such as handling complex queries, null values, schema limitations, and large result sets. Practical deployment involves ensuring production readiness with guardrails and user guidance, addressing schema alignment issues, and managing conversational aspects, underscoring the complexity of implementing RAG in real-world scenarios.