Hallucinations are not random. They follow predictable patterns, and most of them are preventable without touching the model weights. The mechanisms that cause a model to generate plausible-sounding falsehoods are well understood. The interventions that prevent them are engineering decisions, not model choices.
What Causes Hallucinations
Models hallucinate when asked to produce information they do not have with sufficient confidence. The problem is that they are trained to produce fluent, coherent text, and the easiest way to produce fluent, coherent text on a topic is to continue generating rather than stopping and saying nothing.
Four patterns produce most production hallucinations: asking the model for specific facts it was not trained on, asking it to reason over information it has not been given, asking questions where the confident wrong answer is more fluent than the uncertain correct one, and providing insufficient context for the model to distinguish what it knows from what it is generating.
Technique 1: Give the Model Permission to Not Know
The single most effective hallucination prevention technique is explicit. Tell the model it is acceptable to say "I don't know" or "I cannot find this in the provided documents."
Without this permission, models will fill gaps because the training signal rewards completeness. With it, they will flag uncertainty rather than cover it.
"If you cannot answer this question based on the information provided, say: 'I don't have enough information to answer this.' Do not speculate."
This instruction works because it directly addresses the tension between fluency and accuracy that causes hallucinations.
Technique 2: Force Reasoning Before Answering
Hallucinations increase when models produce answers in a single forward pass without visible reasoning. Requiring the model to reason step by step before giving its final answer catches errors before they become outputs.
"Before answering, identify the key facts in the documents that are relevant to this question. Then provide your answer based only on those facts."
The externalised reasoning step creates a checkpoint. If the reasoning step surfaces an absence of relevant facts, the model is far more likely to acknowledge that absence than to hallucinate an answer.
Technique 3: Require Confidence Thresholds
Instruct the model to only answer when it is confident. This sounds simple but is underused.
"Only provide an answer if you are highly confident it is accurate based on the provided information. If you are uncertain, say so explicitly and explain what you are uncertain about."
The key implementation detail is that "confident" needs to be operationalised. Provide examples of what a confident answer looks like versus an uncertain one. Showing the model what uncertainty looks like in practice is more reliable than instructing it to be uncertain in the abstract.
Technique 4: Quote Before You Conclude
For document-grounded tasks, require the model to find and quote the relevant passage before drawing any conclusion. This is the most powerful structural technique for RAG applications.
"Before answering, find the specific sentence or passage from the documents that supports your answer. Quote it exactly. If you cannot find a supporting passage, say so."
When the model cannot find a quote, it is forced to acknowledge that absence rather than generate an answer from general knowledge. This single instruction reduces hallucinations in document-based question answering significantly in practice.
Combining the Techniques
These four techniques compound. A prompt that gives the model permission to not know, requires step-by-step reasoning, asks for confidence flagging, and requires source quotation before conclusions is dramatically less likely to hallucinate than a prompt that does none of these things.
The underlying principle is the same across all four: reduce the pressure the model feels to produce a complete, fluent answer at all costs. Hallucinations are a confidence problem. The interventions that work are the ones that give the model an alternative to false confidence.
What These Techniques Do Not Fix
They do not fix factual errors in the model's training data, do not prevent hallucinations on topics where the model has strong but incorrect priors, and do not substitute for retrieval when the required information is not in context.
They are prompt-layer interventions that address the structural causes of hallucination in production. Combined with good retrieval architecture and clear scoping of what the model is and is not expected to know, they cover the majority of production hallucination failure modes.