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Short answer: the evidence does not show that typos reliably break LLM prompts, and it does not show that one missing quotation mark reliably changes an answer. What the studies do show is narrower and more useful. Prompt formatting and small presentation changes can shift model performance in the settings tested, and the size of that shift depends on the model, the task, and how results are measured.
What the studies actually tested
Most of the work behind this question concerns how prompts are formatted, phrased, and templated, not whether a person made a spelling error. Three sources matter most. The first is a 2024 paper by Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr, presented at the International Conference on Learning Representations (ICLR) in 2024. The second is a 2024 arXiv preprint by He and colleagues that compared prompt templates across tasks. The third is a 2025 Wharton Generative AI Labs report by Lennart Meincke, Ethan Mollick, Lilach Mollick, and Dan Shapiro, dated March 4, 2025. A 2025 paper by Mikhail Seleznyov and colleagues, published in Findings of EMNLP 2025, adds robustness methods and additional tests on newer models.
None of these sources isolates ordinary spelling mistakes as a variable. That gap matters, because “typo” can mean several different things in a prompt, and the evidence treats them differently.
Do typos break LLM prompts?
The reviewed studies do not establish that spelling errors are harmless, and they do not establish that they are damaging. Spelling variation was not separately measured in the summaries available, so any blanket claim in either direction goes beyond the evidence.
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It helps to separate four kinds of change, because they can differ in whether they alter the instruction a model receives:
- Spelling errors in ordinary words (for example, “recieve” for “receive”). The sources do not isolate this case. Modern models are usually trained on noisy text, so a misspelled word often still carries its meaning, but that is an expectation, not a measured result.
- Punctuation and delimiters (quotation marks, brackets, commas that separate list items). These can define where a quoted string, code value, or example begins and ends, which is exactly where a change can matter.
- Formatting structure (Markdown headers, bullet lists, JSON or YAML wrappers). The formatting studies below measured these directly.
- Instruction wording (changing “list three” to “name a few”). This changes the requested task, not just its surface form.
When someone asks whether a typo “breaks” a prompt, the useful question is which of these four categories the error falls into and whether the model’s output depends on it.
Can one missing quote mark change an AI answer?
It can, in principle. Whether it does in a particular prompt is an empirical question that the reviewed evidence does not answer for this specific case. No source measured the effect of deleting a single quotation mark, and none reports a repeatable, quantified change from that edit on current models.
The reason the example is plausible is structural. A quotation mark often marks where a value starts and stops. If a prompt asks the model to extract text inside quotes, to treat a string literally, or to read a value from a JSON-like field, a missing closing mark can change how the model parses the boundary. A small edit that changes the boundary of the input is a different kind of change from a misspelled word inside it. Treat the example as a vivid case of a formatting change that could matter, and test it rather than assume it.
How large can formatting effects be?
The two most striking numbers in this area are real, but both are maxima from specific experiments. They should not be read as typical effects or as estimates for typos.
| Study | Model and setting | Reported figure | What it does and does not mean |
|---|---|---|---|
| Sclar, Choi, Tsvetkov, and Suhr, ICLR 2024 | LLaMA-2-13B, few-shot prompting, subtle prompt-format changes | Differences of up to 76 accuracy points | A study-specific maximum across tested formats. It is not an expected drop from a typo and not a general estimate for current LLMs. |
| He and colleagues, arXiv preprint, 2024 | GPT-3.5-turbo, code-translation task, plain text, Markdown, JSON, and YAML templates | Performance varied by up to 40% depending on template | Applies to one model and one task. The paper describes GPT-4 as more robust to these variations; it does not claim GPT-4 is immune. |
The ICLR authors draw a practical conclusion from their numbers: work that evaluates LLMs with prompting-based methods “would benefit from reporting a range of performance across plausible prompt formats, instead of the currently-standard practice of reporting performance on a single format.” That is a statement about how results should be reported, and it applies directly to anyone judging a prompt by one trial.
Why a single test can mislead
A prompt that appears to work once may not work reliably. The Wharton Generative AI Labs report tested each question 100 times. Its authors found that small prompt variations can have question-specific effects, and that these effects often diminish when results are aggregated across questions. The report concludes: “Our results demonstrate that how we measure performance greatly influences our interpretations of LLM capabilities.”
In practice, this means two things. A one-off answer is weak evidence about a prompt. And a difference that shows up on a few items may vanish in aggregate, or may be concentrated in a small set of questions that matter to you. Both patterns argue for repeated runs and for looking at per-item results, not just an average.
Why results do not transfer across models and tasks
The Seleznyov et al. paper, published in Findings of EMNLP 2025, evaluates four robustness methods across eight models from the Llama, Qwen, and Gemma families and 52 Natural Instructions tasks. It also runs additional format-perturbation tests on GPT-4.1 and DeepSeek V3. Its abstract opens with this sentence: “Large Language Models (LLMs) are highly sensitive to subtle, non-semantic variations in prompt phrasing and formatting.”
The sources also disagree in a useful way about whether one format wins everywhere. The ICLR study reports only weak correlation in format performance between models, meaning a format that helps one model may not help another. The GPT-template study reports that no single format was universally optimal, even within the GPT models it examined. Neither result supports choosing a “best” prompt style once and reusing it everywhere.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to test whether a small change matters for your prompt
If a punctuation fix or formatting change seems to affect your output, check it on your own model and task before relying on it. The following steps follow the variables the studies identify.
- Fix the model and version. Record the exact model name and version you are calling. Results from one version do not automatically carry over to another.
- Define the task and the scoring rule. Write down what counts as a correct answer and the threshold for passing. The Wharton report’s central warning is that measurement choices shape the conclusion.
- Create the exact variants. Make the original prompt, the version with the quote mark restored, and the version with the change you are testing. Change only one thing per variant.
- Use a realistic test set. Include enough distinct inputs that you can see whether the effect is concentrated in a few items or spread across many.
- Repeat each variant. Run each prompt many times, following the Wharton study’s approach of repeated trials rather than a single response.
- Report per-item and aggregate results. Look at which items changed and at the overall rate. A difference that appears only in aggregate, or only in a handful of items, calls for different responses.
The table below summarizes what to record for each comparison. The same fields help when comparing published studies, since apparently conflicting findings often differ on these axes.
| Field to record | Why it matters |
|---|---|
| Model and version | Sensitivity varied by model in the studies reviewed, and the ICLR study found weak cross-model correlation in format performance. |
| Task or benchmark | The GPT-template study reports large differences on a code-translation task; other tasks were not reported to show the same size of effect. |
| Exact prompt change | “Formatting” covers many edits. Record the precise character or structure that changed. |
| Number of trials | Single runs cannot separate a real effect from variation between runs. |
| Scoring threshold | Whether an answer counts as correct determines whether a change shows up at all. |
| Per-question or aggregated | Question-specific effects can disappear when averaged. |
What the evidence does not show
The reviewed sources do not show that typos never matter, and they do not show that a single omitted quotation mark always changes an output. The first claim goes beyond the studies, which did not isolate spelling errors. The second goes beyond them too, because no source measured that edit directly on current models. The accurate position is that prompt surface form can matter, that the effect is conditional, and that it has to be measured for each model and task.
The original headline’s contrast is therefore too strong. A better one-line version is that formatting changes can affect LLM answers, and that you should test the change rather than assume it is harmless or fatal.
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