Reduce AI hallucinations through architecture
Reduce hallucinations with grounding, schemas, verification, refusal, authority separation and causal measurement.
A hallucination can originate in the model, missing evidence, failed retrieval or an interface that forces an answer.
Adding prompt instructions may move the problem without guaranteeing exact fidelity to evidence.
The right intervention depends on the observed cause: data, representation, model, verifier or refusal policy.
What the architecture must make explicit
Capability does not grant authority. Critical systems must separate what proposes, verifies, permits and executes.
Move from a keyword to an inspectable engineering decision
Separate
Decouple probabilistic proposal, validation, permission and physical execution.
Bound
Limit tools, data, actions, duration and environments available to each component.
Refuse
Make missing evidence, timeouts and ambiguity explicit states.
Audit
Retain the applied rule, its inputs, version and resulting decision.
A claim is only as strong as its binding
The gate must evaluate the exact output produced by the exact realization. Integrity, scientific validity and authority are related, but never interchangeable.
Questions that change the design
What does reduce ai hallucinations through architecture require?
A hallucination can originate in the model, missing evidence, failed retrieval or an interface that forces an answer. Capability does not grant authority. Critical systems must separate what proposes, verifies, permits and executes.
Which evidence should be retained?
At minimum: exact realization identity, inputs and outputs, protocol version, measurements, failure cases and the decision rule. The right intervention depends on the observed cause: data, representation, model, verifier or refusal policy.
Does AMF prescribe a particular model?
No. AMF treats the model, runtime, data, tools and deterministic components as candidate materials. Their retention depends on measured contribution and declared constraints.