Why Different Prompts Yield Different Results
One Question, Different Answers
Two people ask a model what seems to be the same question. One writes briefly: «Explain quantum entanglement.» The other adds: «Explain quantum entanglement in simple terms, as if I have never studied physics.» The answers will differ significantly – not because the model «guessed the intent», but because it received different input data.
This fundamental observation lies behind everything called prompt engineering, and it requires a precise explanation. It is not about the magic of phrasing or some special «sensitivity» of the system to words. It is about how the model itself is structured and what exactly it processes.
How Language Models Process Prompts as Input Context
Prompt as Context: What the Model Actually Receives
Before discussing the impact of phrasing, we need to understand what the model actually «sees»./p>
A language model does not receive a request as a separate object labeled «this is the user's task».It receives a sequence of tokens – units of text into which any input is broken down. These can be words, parts of words, or individual characters, depending on the specific implementation. Crucially, for the model, there is no fundamental difference between a «request» and «context» – it is a single stream of text that it works with.
The prompt becomes part of the input context. Everything that goes in there influences what the model will generate next: system instructions (if provided), previous dialogue turns, and the text of the request itself. The model does not extract a «main task» from this flow or build an internal representation of a goal. It treats the entire sequence as a single piece of text, the continuation of which must be the response.
This is a pivotal point. A prompt is not a command sent to a system with instructions. It is text for which the model will calculate a continuation. This is exactly why different phrasings yield different results: they create different initial text, and the probable continuations of that text vary.
How Prompt Phrasing Changes Token Probability Distribution
Probabilistic Shift: How Phrasing Changes the Output
A language model is trained on vast arrays of text. During training, it forms internal representations of which sequences of words appear together, in what contexts certain constructions emerge, and what style is typical for a particular genre. None of this is stored as explicit rules – it is encoded in the model's weights and in how it processes input text.
When a model generates a response, it sequentially selects the next token based on a probability distribution. This distribution depends on the entire input context. Changing the phrasing – even slightly – alters this context, thereby shifting the distribution of likely continuations.
Consider a specific example. The request «Write a text about the harms of sugar» and the request «Write a popular science article about the harms of sugar for readers without a medical education» create substantially different contexts. The second request contains signals statistically linked to specific types of texts in the training data: structured materials with explanations, accessible language, and a certain degree of detail. The model does not «decide» to write differently – it processes a more specific context in which the likely continuations are skewed toward a certain style and structure.
Constraints in a request work similarly. The phrase «without technical terms» is not a prohibition in the programming sense. It is a textual signal that changes the context. In training data, texts following or containing such phrasing are more often written in simplified language. The model continues the text according to what is statistically justified for that given context.
The structure of the request influences things for the same reasons. If a request is written in a formal style, it creates a context where a formal response is statistically more probable. If a request contains numbering or explicit sections, it shifts the probabilities toward a structured answer. This is not because the model «adapts» to the user in a conscious sense, but because such patterns are ingrained in its training.
Why LLMs Process Textual Signals Instead of User Intent
Interpretation Without Understanding: Where the Line Is Drawn
It is important to clearly define what happens when a model «follows an instruction» and what does not.
When a request says «answer briefly», the model does not make a decision to be brief. It processes text containing that signal and generates a continuation that statistically matches contexts with similar phrasing. In most cases, such a continuation will indeed turn out to be shorter – because in the training data, texts following such markers were more often brief.
This is not interpretation in the sense of comprehending a goal. It is the processing of a textual signal.
The difference is substantial. A human, given the instruction «answer briefly», can understand that they need to concisely convey the essence even in an atypical situation where the rules are unclear. They relate the instruction to the intent and adapt it to the context through understanding. The model does not do this. It does not build a model of the user's intent. It does not reason about what the author of the request wanted to say. It calculates a statistically justified continuation for a given sequence of tokens.
Several important practical conclusions follow from this.
First, the model reacts to what is written, not what was implied. If the phrasing of a request creates a context statistically associated with a certain type of text, the model will generate exactly that – regardless of whether that was the user's intent. Poor phrasing is not compensated for by «correct intent», because the model has no access to intent.
Second, contradictions in a request are not resolved logically. If a prompt contains mutually exclusive requirements, the model will not detect the conflict or ask for clarification in the sense of recognizing a problem. It will process the context and generate a continuation that, to some extent, «satisfies» different parts of the request – or follows the part whose signals proved statistically stronger.
Third, implicit assumptions in a request affect the answer. If a question is phrased to contain a hidden premise, the model will likely accept it as a given – because texts continuing such constructions most often do not challenge them, but rather expand upon them. This is neither trust nor a lack of critical thinking; it is a statistical pattern.
Conclusion: Instruction as Input Data
A prompt is not a command received and executed by a conscious system. It is input data from which a calculation begins.
A language model does not store the «user's task» as an object and return to it during the generation process. It sequentially processes context and selects probable continuations. Everything that ends up in this context – phrasing, style, structure, explicit constraints, implicit signals – affects the probability distribution and, consequently, the result.
This means that changing the phrasing is not a trick or a way to «fool» the system. It is a change in input data that leads to a change in output. The mechanism here is fundamentally the same as in any other data processing system: different input, different output.
The boundary that is important to maintain is this: the model processes text, it does not comprehend a goal. When a request yields the expected result, it means the phrasing created a context statistically linked to the desired type of response. When the result is unexpected, the phrasing created a different context, and the probabilities worked out differently.
Understanding this mechanism does not make working with the model any less useful. On the contrary, it allows one to more accurately align expectations with the actual design of the system – and to avoid attributing either intent or understanding where there is only a statistically justified continuation of text.