Engineers Advised to Treat LLM Pipeline Steps as Unreliable External Dependencies

Software teams are increasingly incorporating large language model calls into data pipelines for tasks like classification. Experts recommend treating these calls as flaky external APIs, implementing timeouts, retries, and cost ceilings. To ensure reproducibility and control costs, they advise caching results by input hash and pinning specific model and prompt versions. Finally, outputs should be gated with pre-defined quality checks, monitoring for validation rates, accuracy, and output drift before reaching production.