Copernicus is designed to fundamentally avoid hallucination through a strictly deterministic pipeline. Both the agent logic and the Ask interface generate responses exclusively from secure, locally ingested data combined with explicit, fixed rules — never from open-ended or probabilistic text generation.
Given identical inputs (the same ingested dataset, the same prompt, and the same agent configuration), the system produces fully reproducible outputs. This is because the core response path contains no stochastic sampling or random generation steps.
While external time-varying sources (such as live market feeds or third-party APIs) may naturally lead to different numerical results when the underlying data or observation date changes, this variability is entirely orthogonal to the model’s internal computation and does not introduce hallucination.
In essence, Copernicus is not generative by default. It computes answers directly from the supplied data and specified procedures, rather than inventing unconstrained natural-language content.
Why this approach works better:
Ends with a crisp, memorable summary of the core principle.
It leads with the anti-hallucination benefit.
Uses stronger, more direct language (“fundamentally avoid hallucination”, “strictly deterministic”).
Clearly contrasts the deterministic local path with any external variability.