How to fix: model gives inconsistent / non-deterministic outputs
Cause
LLMs are inherently probabilistic; outputs vary across runs, and small prompt or model changes shift behavior.
The fix
- 1Use structured outputs to constrain format so at least the shape is consistent.
- 2Lower variance where supported (e.g. lower effort or sampling) — note that no setting guarantees identical outputs.
- 3Pin the model version explicitly so a provider update doesn’t silently change behavior.
- 4Add evals so you can measure consistency and detect when a change shifts it.
- 5For pipeline steps that need determinism, validate and, if needed, retry against a schema.
Prevent it
Pin model versions, constrain output with schemas, and cover critical routes with evals so behavior is measured, not assumed.
Frequently asked questions
What causes “model gives inconsistent / non-deterministic outputs”?
LLMs are inherently probabilistic; outputs vary across runs, and small prompt or model changes shift behavior.
How do I prevent “model gives inconsistent / non-deterministic outputs” from recurring?
Pin model versions, constrain output with schemas, and cover critical routes with evals so behavior is measured, not assumed.