Reliability

LLMs rely on probabilities, not fixed rules. Why do AI chatbots sometimes give different answers even if you ask the same question? The short answer: they’re not just simple machines running a single fixed script. They’re giant neural networks trained on massive amounts of text. They look at what you say, break it into parts, and guess the most likely next word. Depending on how the guessing is done, you can get different responses.

Temperature settings control randomness. If you set something called the “temperature” to zero, the chatbot picks the most likely words every time. This often makes it give the same answer for the same prompt. But even then, small changes—an extra space, a comma, or a line break—can affect the result. This can feel as if the system is unpredictable.

Think of LLMs as people, not programs. The truth is, large language models (LLMs) are more like people than old-school computer programs. They’re not truly “thinking,” but they’re also not strictly predictable. Just like when people answer questions, small differences in what you say to them can change their reply. If you think of them as interns on your team rather than simple tools, you can use the same strategies you’d use with people to get better results.

Clearer instructions mean better results. For instance, if an intern doesn’t do a task correctly, maybe your instructions weren’t clear. You might give examples, give step-by-step instructions, ask them to explain their reasoning, or refine your directions so it’s harder to misunderstand. This is called “prompt engineering.”

Ask for proof. Ask them to cite their answers. For example, a link to the source, or the verbatim text from the source. It's easy to verify if they made up a source.

Get a second opinion. You can also have another “intern” (another model) review the first one’s work, or ask multiple models to answer the same question and compare results. This is often called an “agentic approach.”

In short: LLMs vary their answers because they’re complex, probabilistic systems. To handle this, give clearer prompts, simplify the question, compare answers from multiple models, or set parameters like temperature to minimize randomness. Each of these strategies helps you tame the LLMs—just as you’d manage and coach a team of human assistants.