Rhod Arriola

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AI delivery fails because no one owns the data quality before it touches the model In most enterprises, data is ingested without cleaning or validation, leading to noisy embeddings and unreliable retrieval. Teams often rely on defaults for chunking or skip defining relevance thresholds, assuming the model will handle it. This lack of ownership creates fragile pipelines where small data issues cascade into major failures. A disciplined approach requires clear data stewardship, defined quality gates, and stakeholder…

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