How do you ensure reproducibility in LLM systems?

Reproducibility is ensured using versioned models, prompts, embeddings, and deterministic inference settings.

Unlike traditional ML, LLM outputs can vary due to temperature, sampling, and backend changes. Reproducibility requires strict version control of models, prompts, retrieval indices, and inference parameters. Logging full execution traces also allows replaying requests under identical conditions.