What to Do When Claude Hallucinates
What to Do When Claude Hallucinates
In collaborating with an AI, the vending machine model is intuitive and wrong. You put in a prompt. An output comes out. You use it. That works well enough for low-stakes tasks — summarize this, reformat that, draft a quick reply. It breaks down the moment the output goes somewhere that matters. Because the model does not know the difference between a confident correct answer and a confident wrong one. It delivers both the same way. The human on the receiving end has to supply the judgment the model withheld.
This is not an argument against using AI. It is an argument for using it as a tool rather than an oracle. The distinction sounds simple. In practice, most people are still operating the vending machine. They prompt, they collect the output, they move on. When the output is wrong — and it will be wrong — they have no methodology for handling it. This piece is that methodology.
Confident Is Not the Same as Correct
AI outputs arrive with uniform confidence. There is no shakiness in the voice, no hedge built into the delivery, no asterisk flagging the sections where the model is working from thin training data or making an inferential leap it cannot fully support. The prose is smooth. The structure is clean. The answer sounds authoritative because that is the only register the model has.
This is the fundamental characteristic a human user must internalize before working seriously with any AI tool. Confidence is a stylistic property of the output. Correctness is a factual property of the claim. They are independent variables. A hallucination does not announce itself. It reads exactly like a well-sourced, well-reasoned response. The difference is not in the text. It is in the underlying reality the text may or may not reflect.
The professional who understands this brings informed skepticism to every output. Not hostility. Not blind trust. The specific, calibrated skepticism of someone who knows that the model is always confident and only sometimes right.
Confidence is a stylistic property of the output. Correctness is a factual property of the claim. They are independent variables.
The Suspected Hallucination
A suspected hallucination is not an obvious error. If it were obvious, you would not need a methodology — you would simply discard it and move on. A suspected hallucination is a confidently stated output that you cannot yet verify. It may be right. It may be wrong. It may be something in between. Your posture toward it is the same regardless: informed skepticism, held steady, until you have done the work of evaluating it.
This is where most AI users stop. They flag the output as potentially wrong, set it aside, and either prompt again or give up on the question. That is the vending machine user's response to a broken machine. It treats the output as binary — either usable or not — and discards everything in the suspected hallucination the moment doubt enters the picture.
The methodology proposed here starts exactly where that instinct says to stop. The suspected hallucination is not the end of the process. It is the beginning of a different one.
Could It Be Right?
The first question is the one nobody asks. Could this be right? Not in every particular, necessarily. But in part. In structure. In direction. A hallucination can be wrong on the specific facts and right on the underlying logic. It can produce a conclusion that does not match reality and still point toward something that does. Discarding it wholesale because one element is wrong throws away every useful signal along with the noise.
Push the question further. Could this be right under certain conditions? Under certain circumstances? A statement that is wrong as a general rule may be accurate in a specific context, for a specific use case, within a specific set of constraints. The model does not know your context. It answered a general version of your question. The answer may fit your situation better than a first reading suggests — or worse. You will not know until you ask.
Maybe yes. Maybe no. But the asking is not optional. It is the first move in a serious evaluation.
Valid but Weak
The most useful category in this methodology has no standard name in the AI discourse. Call it valid but weak. Not wrong. Not right. Directionally sound but insufficiently developed. The hallucination that lands here is not a failure to be discarded. It is a draft to be strengthened.
Valid but weak means the model got the shape of the answer right and got the substance thin. It identified the right problem and underspecified the solution. It pointed at the correct territory and gave you a rough map when you needed a detailed one. That is genuinely useful. The shape is hard. The substance can be developed — by you, by a better-constructed follow-up prompt, by bringing the output to a domain expert who can fill in what the model left thin.
Naming the category matters because it changes what you do next. Wrong sends you back to zero. Valid but weak sends you forward with material to work with.
Valid but weak is not a failure to be discarded. It is a draft to be strengthened. The shape is hard. The substance can be developed.
What Is Usable?
Once you have assessed whether the output could be right and whether it lands in valid-but-weak territory, the next questions are practical. What is actually usable here? What can you do something with right now? What rises to the level of actionable — meaning it can inform a decision, change an approach, or move a project forward?
This is where the human adds value the model cannot. The model produced the output. The human reads it with context the model did not have: knowledge of the specific situation, the constraints in play, the people involved, the history of the problem. That context is the filter. Run the output through it. What survives is what is usable. What survives and points toward a next step is what is actionable.
The vending machine user never gets here because they either used the output without filtering it or discarded it the moment suspicion entered the picture. The human who treats AI as a tool rather than an oracle reaches this step routinely. It is where the real work happens.
Was It the Prompt?
The final question closes the loop. Before you move on, ask whether the output could have been better with a better prompt. This is not always answerable. Claude and Claude Code are black boxes. You cannot inspect the weights, examine the training data, or trace exactly why a particular output came out the way it did. The model's reasoning is not fully visible to you and in some respects is not fully visible to anyone.
But the prompt is yours. You wrote it. You can examine it. You can ask whether the question was precise enough, whether you gave the model sufficient context, whether you asked for the right thing or a version of the right thing that the model had to interpret its way into. A vague prompt produces a confident answer to a question you did not quite ask. That is not a hallucination in the technical sense. It is a mismatch between what you needed and what you requested.
Sometimes the prompt was fine and the model simply did not have what was needed to answer well. Sometimes a better prompt would have produced a materially better output. You will not always know which. But asking the question is how you improve the next prompt. The loop closes and the methodology carries forward.
The Human Is the Editor
The vending machine model positions the human as an operator. Insert prompt, collect output, dispense result. The methodology proposed here positions the human as an editor. The AI is a fast, confident, occasionally wrong contributor who produces first drafts at a pace no human can match. The human's job is not to trust the draft or discard it. The human's job is to edit it.
Could it be right? Could it be right under different conditions? Is it valid but weak? What is usable? What is actionable? Was it the prompt? Those six questions applied consistently to any suspected hallucination produce a working methodology for getting value out of AI output that the vending machine model leaves entirely on the table.
The model will always be confident. Whether it is correct is always your question to answer.
Aaron Rose is a technology writer and publisher covering system architecture, cloud platforms, and AI tools.