A recent technical analysis within the Enterprise Document Intelligence series redefines how development teams approach Retrieval-Augmented Generation failures. The findings challenge the industry standard that large language model hallucinations stem from generation flaws, demonstrating instead that the vast majority originate from upstream retrieval pipeline defects. By isolating the retrieval component and holding the foundation model constant, the research proves that the retrieval stage dictates the boundaries of model output, effectively determining what the system can invent. To validate this hypothesis, researchers deployed a standard NIST Cybersecurity Framework v1.1 corpus against a naive cosine-similarity embedding pipeline. When queried on ransomware backup practices, the correct response residing on page 41 ranked last out of fifty-five pages. The embedding model averaged out the critical term backup across broader semantic similarities to data security and incident recovery, pulling irrelevant categories to the top. A simple keyword frequency count subsequently ranked the identical page first. This controlled experiment isolates retrieval as the sole variable responsible for the error, proving that the model was answering faithfully from misaligned context rather than fabricating information. The analysis categorizes RAG hallucinations into three distinct retrieval failure modes. The first is a direct recall failure where the relevant segment falls outside the retrieval window, forcing the model to rely on pretraining data. The second involves a misleading passage that semantically aligns with the query but addresses a different control, often surviving initial review due to plausible citations. The third occurs when correct context is buried beneath high-frequency distractors, causing the model to conflate closely related terms. In all three scenarios, tightening generation parameters or upgrading to larger models yields negligible improvement, as the root cause remains the context supplied. Cosine similarity struggles on enterprise documentation because it prioritizes surface-level semantic overlap over precise lexical matching. The recommended remedy shifts retrieval from broad semantic search to targeted span anchoring. By employing parallel detection signals including expert-validated keyword dictionaries, hierarchical document structure, and embeddings as a supplementary vote, systems can accurately locate the correct information anchor. Once identified, context must be tightly scoped to that specific span and its immediate structural neighbors, eliminating noise that triggers blending errors. For cases where retrieval alone cannot guarantee accuracy, the briefing advocates implementing a typed answer contract as a secondary validation layer. This mechanism requires the generation model to output a structured value, a direct evidence span from the retrieved context, and a confidence metric. Any response lacking a verifiable context span is automatically flagged, preventing ungrounded outputs from reaching end users. The findings carry significant implications for enterprise AI deployment, where precision outweighs fluency. By treating hallucinations as retrieval diagnostics rather than model deficiencies, development teams can reallocate resources toward pipeline architecture. Measuring answer rank before generation runs, implementing multi-signal anchoring, and enforcing strict context scoping resolve the majority of grounded pipeline failures. The research and reproducible measurement tools are available through the accompanying technical series repository.