Exactly. We treat them like databases, but they are hallucination machines.
My thesis isn't that we can stop the hallucinating (non-determinism), but that we can bound it.
If we wrap the generation in hard assertions (e.g., assert response.price > 0), we turn 'probability' into 'manageable software engineering.' The generation remains probabilistic, but the acceptance criteria becomes binary and deterministic.
but the acceptance criteria becomes binary and deterministic.
Unfortunately, the use-case for AI is often where the acceptance criteria is not easily defined --- a matter of judgment. For example, "Does this patient have cancer?".
In cases where the criteria can be easily and clearly stipulated, AI often isn't really required.
You're 100% right. For a "judgment" task like "Does this patient have cancer?", the final acceptance criteria must be a human expert. A purely deterministic verifier is impossible.
My thesis is that even in those "fuzzy" workflows, the agent's process is full of small, deterministic sub-tasks that can and should be verified.
For example, before the AI even attempts to analyze the X-ray for cancer, it must:
1/ Verify it has the correct patient file (PatientIDVerifier).
2/ Verify the image is a chest X-ray and not a brain MRI (ModalityVerifier).
3/ Verify the date of the scan is within the relevant timeframe (DateVerifier).
These are "boring," deterministic checks. But a failure on any one of them makes the final "judgment" output completely useless.
steer isn't designed to automate the final, high-stakes judgment. It's designed to automate the pre-flight checklist, ensuring the agent has the correct, factually grounded information before it even begins the complex reasoning task. It's about reducing the "unforced errors" so the human expert can focus only on the truly hard part.
I don't agree that users see them as databases. Sure there are those who expect LLMs to be infallible and punish the technology when it disappoints them, but it seems to me that the overwhelmingly majority quickly learn what AI's shortcomings are, and treat them instead like intelligent entities who will sometimes make mistakes.
They didn't claim to know it, they said "it seems to me". Presumably they're extrapolating from their experience, or their expectations of how an average user would behave.
My thesis isn't that we can stop the hallucinating (non-determinism), but that we can bound it.
If we wrap the generation in hard assertions (e.g., assert response.price > 0), we turn 'probability' into 'manageable software engineering.' The generation remains probabilistic, but the acceptance criteria becomes binary and deterministic.