Working with AI engineering teams for years has shown me a consistent pattern. Most of the time isn’t spent on model. It’s spent on repetitive workflow steps. - Ingestion: data formats vary, cleaning rules stay the same - Chunking: simple segmentation but breaks easily when inconsistent - Metadata alignment: structural drift forces manual fixes - JSON validation: mechanical corrections to model output - Eval setup: repeated patterns across every project - Tool contracts: predictable inputs and outputs - DAG wiring: same templates, different logic - Logging and fallback: always required, rarely complex
These steps repeat because they aren’t deep-skill tasks, but they hold the system together. What are the repetitive parts of your AI workflow that slow you down the most?
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