Hallucinations in GPTs and LLMs: Causes, Triggers and Prevention

LLMs • Wednesday, Jul 30, 2025

Explore what AI hallucinations are, why large language models hallucinate, how to trigger them and strategies to avoid them.

The term hallucination in artificial intelligence refers to a response generated by a model that contains false or misleading information but is presented as if it were fact. Unlike a simple error, a hallucination is often plausible and convincing. The Wikipedia entry on hallucinations in AI notes that chatbots powered by large language models can embed plausible‑sounding falsehoods in their responses. Because users tend to trust the fluent language of these systems, hallucinations pose real risks in domains like customer service, education and healthcare.

Why do LLMs hallucinate? One core reason is architectural constraints. Language models process tokens sequentially and have a fixed attention window, meaning they cannot attend to all previous context simultaneously. As the sequence gets longer, the model forgets earlier parts of the conversation, leading to drift and compounding errors. This limitation can cause the model to drop crucial information or invent details to bridge gaps. Additionally, because the model generates one token at a time, any early mistake can snowball into a fully fabricated narrative.

Another cause of hallucination is the probabilistic nature of generation. LLMs do not truly understand content; they are sophisticated pattern matchers. They produce the most likely next word based on statistical associations learned during training. When confronted with ambiguous or underspecified prompts, the model may interpolate or extrapolate based on patterns rather than facts. Exposure bias (training on predictable continuations) and data gaps in the training corpus contribute to this behavior. Rare topics or domain‑specific queries fall outside the model’s experience, increasing the likelihood of hallucination.

Hallucinations can be triggered deliberately or inadvertently. Ambiguous questions like “Tell me about the Emperor of Mars” invite the model to invent details because no such historical figure exists. Conflicting instructions or contradictory context can also induce hallucinations as the model tries to satisfy all constraints. Real‑world examples highlight the problem: Air Canada’s chatbot offered refunds that did not exist, resulting in a court case, and Google’s Bard misreported the James Webb Space Telescope’s first discoveries. Even subtle cues like asking the model to be creative or to “imagine” something can encourage it to fabricate.

Given these risks, how can we reduce hallucinations? Mitigation strategies operate at different layers of the pipeline. At the input layer, we can rewrite user queries to be clear and unambiguous, supplying necessary context and constraints. Injecting relevant knowledge into the prompt (e.g., by including a brief reference passage) and using chain‑of‑thought prompting, which encourages the model to reason step by step. Clarifying the question and narrowing the topic reduces the search space and helps the model stay grounded.

At the design layer, retrieval‑augmented generation (RAG) addresses hallucinations by coupling LLMs with external knowledge sources. Before generating a response, the system retrieves documents from a database or search engine and conditions the model’s output on this evidence. This grounding mechanism ensures that the model’s response is anchored in real information. Rule‑based filters can further scan outputs for factual inconsistencies or out‑of‑domain content【74489523965145†L184-L221】.

At the output layer, human oversight remains critical. For high‑stakes applications, implement validation steps where domain experts review model outputs before they are delivered to end users. Encourage users to treat LLM responses as drafts rather than authoritative answers. Logging and feedback loops can help identify recurring hallucination patterns and inform model fine‑tuning or prompt engineering adjustments.

Ultimately, hallucinations are an inherent byproduct of using statistical models for open‑ended language generation. While we can reduce their frequency and severity through better prompts, retrieval and validation, we cannot eliminate them entirely. Awareness of the causes and triggers of hallucinations empowers users and developers to design safer interactions. By combining LLMs with curated knowledge bases and human review, we can harness the creativity and fluency of these models while mitigating the risks of fabricated content.