A language model can produce a convincing answer that does not satisfy the task. The gap becomes visible when the work needs an exact value, an authoritative source or a verified change in another system.
Reliability comes from testing the complete application and constraining what it can do. Neither a more polished prompt nor a longer explanation establishes that the result is correct.
Define correctness for the task
Different tasks need different checks. A classification may have a fixed set of allowed values. A financial preparation workflow may need source-linked inputs, reproducible calculations and a qualified reviewer. A knowledge answer may need a current supporting passage and an explicit limit when the source is silent.
Write those criteria before choosing a headline accuracy metric. An average score can hide failures in a rare but consequential category.
Include ambiguous, incomplete and out-of-scope inputs. If the system must ask for clarification, test whether it actually does rather than inventing a plausible missing value.
Validate outside the model
Schema validation can confirm that an output has the expected shape. Business rules can check allowed values and relationships. Calculations can be rerun in tested software.
These checks address different questions. Valid JSON does not establish a correct amount. A permitted category does not establish that the category applies to this document. Keep source references and review steps where interpretation remains necessary.
Tool requests need the same separation. The model proposes a request; application software validates and authorises it before execution. The connected system’s response must then be checked against the intended operation.
Check retrieval and generation separately
RAG can supply relevant evidence without changing model weights. It does not eliminate unsupported statements, stale sources or misinterpretation.
Test whether retrieval finds current authorised passages, then whether the answer is supported by them. Examine citations at the sentence or claim level where the task needs that detail. A real link attached to an answer is not sufficient proof.
Keep source updates and access revocation in the operating process. A model cannot compensate reliably for an index that continues returning a superseded policy.
Be precise about confidence and explanation
A model’s statement that it is highly confident is not automatically a calibrated probability. Any score used to route work needs a defined method and validation on the target task.
Likewise, an explanation written by the model is not a complete audit of its internal computation. Research on chain-of-thought faithfulness shows why generated reasoning should not be equated with a trustworthy causal account.
Record observable evidence: inputs or safe references, selected sources, configuration, validation results, tool requests, outcomes and human decisions. Protect those records according to their sensitivity and purpose.
Make review a real control
A reviewer needs the source material, proposed changes and relevant checks before deciding. Approval should identify a person and the exact proposal they approved.
Human review also has limits. People can misunderstand evidence, become overloaded or accept a plausible result too quickly. Design the interface and workload so review is useful, and assess errors rather than assuming that a human step guarantees correctness.
For regulated professional work, the responsible professional retains the decision. The application can prepare and organise information without claiming to replace that judgement.
Maintain the evidence after launch
Keep a representative regression set and review it after changing the model, prompt, retrieval configuration or tools. Monitor the failure categories that matter to the business, including work that stalls or requires correction.
Use bounded retries and clear escalation paths. A stopped task with an honest explanation may be safer than an apparently successful run whose external effects are unknown.
Our AI Security and Governance and AI Software Engineering pages cover these complementary responsibilities. Reliable operation is something to measure and maintain, not an adjective supplied by the model.
Written by Raj Dalal, Advisor at Ryzolv.
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