The artificial intelligence development community is increasingly addressing a critical vulnerability in Retrieval-Augmented Generation workflows: evaluation overfitting. A prevalent scenario across engineering teams involves deploying RAG applications that report near-perfect benchmark scores following iterative testing. While this refinement process appears methodical, it frequently compromises the integrity of the evaluation pipeline, causing test datasets to function as implicit training data and severely diminishing real-world system reliability. Standard machine learning protocols require strict separation between training, validation, and held-out test sets. The test set remains completely isolated to gauge a model capacity to generalize to unseen inputs. RAG evaluations, however, routinely breach this boundary. Engineers commonly optimize system prompts, tune retrieval parameters, and curate question-answer pairs through repeated exposure to identical evaluation benchmarks. This cycle prompts the AI to memorize specific test cases rather than learning adaptable reasoning patterns. Typical contamination methods include selectively testing scenarios the system already handles, drafting queries directly from indexed source documents, or persistently tweaking prompts until metrics reach an artificial plateau. Consequently, the deployed application performs flawlessly on paper but degrades when processing novel user requests in live environments. This trend closely mirrors Goodhart Law, which dictates that a metric ceases to be useful once it becomes a target. Within AI development, this manifests as reward hacking, where optimizing for a specific evaluation score overrides the actual objective of building a robust, production-ready system. The risk is particularly insidious because overfitting masquerades as diligent engineering during development. Performance discrepancies only surface upon production deployment, where the system struggles with uncurated, real-world data. Addressing this challenge demands rigorous protocol adherence. Teams must establish a strictly held-out test set reserved exclusively for final deployment validation. Evaluation queries must be drafted independently of the knowledge base and must accurately reflect unpredictable user intent. Engineering leadership should treat exceptionally high benchmarks with skepticism, recognizing them as potential indicators of dataset contamination rather than genuine capability. Prioritizing process discipline over metric maximization ensures that RAG applications maintain consistent accuracy under production loads. As enterprise AI integration accelerates, safeguarding evaluation methodology remains as critical as model architecture.