AI hallucinations cannot be managed by one prompt or retrieval step. Reliable use requires evidence, task design, evaluation and operating controls.
The answer depends on the task and the error
Hallucination is commonly used for generated content that is unsupported, fabricated or inconsistent with the required evidence. The term can cover different failures: inventing a fact, misreading a source, combining two entities or claiming an action completed when a tool did not confirm it. Those failures have different causes and controls. A single claim that hallucination is solved or inevitable is therefore not useful for an operating decision.
Retrieval helps but does not guarantee grounding
Retrieval can place relevant documents in context, but the system may select the wrong passage, ignore a qualification or cite evidence that does not support the conclusion. Source versions and permissions also matter. A grounded workflow should preserve the evidence span, source and retrieval time, and it should abstain or escalate when the available material cannot establish the answer.
Determinism is not the same as truth
Lower sampling variability can make repeated outputs more consistent. It does not correct an incorrect source, an ambiguous instruction or a faulty tool state. Conversely, variation can be useful for exploration while being unacceptable for a financial, clinical or safety decision. The required consistency follows from the task and consequence.
Agents add action failures to content failures
An agent may retrieve, plan, call tools and change a business system. The final message can be accurate while the agent reserved the wrong item or created a duplicate payment. Evaluation must inspect the complete trajectory and resulting state. Deterministic checks should establish identities, amounts, permissions and completion where those facts are observable.
Reliability comes from a controlled system
Marketways defines the release claim, evidence, permitted tools, human authority and final state. We build ordinary, ambiguous and adverse scenarios, validate graders, repeat variable cases and retain production failures as regression tests. AI evaluation and assurance does not promise error-free AI. It establishes which behaviour has been demonstrated, which failures block release and how the organisation will detect change.
