--- title: "Why Do AI Models Give Different Answers to the Same Factual Prompt?" description: "Why Do AI Models Give Different Answers to the Same Factual Prompt?" author: "Otto Lehtonen" published: 2026-10-15 updated: 2026-10-15 canonical: https://answers.mindstick.com/qa/117608/why-do-ai-models-give-different-answers-to-the-same-factual-prompt category: "Mix Category" tags: ["artificial intelligence", "AI Tools", "Fact Checking"] reading_time: 2 minutes --- # Why Do AI Models Give Different Answers to the Same Factual Prompt? I tried asking two AI assistants the same straightforward factual question and got answers that disagreed on a key detail. Neither response sounded uncertain, which made it hard to tell whether one model had better information or whether the wording was steering them in different directions. What causes this kind of disagreement? I’m curious how training data, model settings, and access to current sources affect answers to the **same factual prompt**, and what checks you use before trusting a claim. ## Answers ### Answer by Alessandro Bianchi It can be surprising when the same AI model answers a factual question differently from one attempt to the next. Usually, that reflects how the model generates text—not a deliberate change in what it “believes.” AI models predict a likely sequence of words based on the prompt and the conversation so far. Several things can shift that prediction: - **Randomness in generation:** Sampling settings, including temperature, can make the model choose different wording or facts from among plausible options. With temperature set low, answers are generally more consistent, though not necessarily guaranteed to be identical. - **Small changes in context:** A preceding message, slightly different wording, or a long conversation can alter how the model interprets the question. **Ambiguous wording in a prompt** can also lead it to answer different interpretations. - **Different models or updates:** A service may switch model versions, system instructions, or **model version and training data**. The model may also lack current information if the fact has changed since its training. - **External information:** Some systems look things up before responding. Different **retrieval sources and search results** can produce different answers, even when the user’s prompt is unchanged. There’s also a distinction between a consistent answer and a correct one: a model can repeat the same mistake every time. For important facts, ask it to cite sources, check those sources yourself, and specify details such as a date or jurisdiction. If you’re testing repeatability, use the exact same prompt, model, and settings, and start a fresh conversation each time. --- Original Source: https://answers.mindstick.com/qa/117608/why-do-ai-models-give-different-answers-to-the-same-factual-prompt Copyright © MindStick Software Pvt. Ltd. This Markdown version is provided for developers, AI systems, and offline reading.