Prompt Engineering Finally Proves That Prompt Repetition Gives Better Answers But Only If You Do So These Ways

Prompt engineering finally acknowledges that prompt repetition is a reputable technique and deserves its rightful place in your prompting toolkit.

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In today’s column, I examine a prompt engineering technique that has been commonly thought to be useful, but for which little or no solid research has supported its use. The usual scuttlebutt has been that there is no demonstrative value in using the technique.

Well, the story takes a new turn. A happy, upbeat turn. Turns out that a recently released research study showcased that the prompt engineering technique is worthy and can go ahead and be utilized. You might be wondering what technique I’m referring to – it’s the “prompt repetition” technique.

Prompt repetition refers to the idea that when you enter a prompt, you immediately concatenate that very same prompt so that the prompt itself is doubled up. For example, instead of a prompt saying, “Tell me the square root of 25”, you would say it twice, as in “Tell me the square root of 25. Tell me the square root of 25.” I know it looks odd. You are probably thinking it seems ridiculous. Possibly absurd.

Maybe it looks that way, but the results are that by-and-large you will get better answers from AI accordingly. Not always. And there are various circumstances in which it not only won’t help, but it could also undercut the results. No worries, since I will lay out the particulars for you.

Let’s talk about it.

This analysis of AI breakthroughs is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here).

Prompt Engineering Essentials

Readers might recall that I previously posted an in-depth depiction of over eighty prompt engineering techniques and methods (see the link here). Seasoned prompt engineers realize that learning a wide array of researched and proven prompting techniques is the best way to get the most out of generative AI and large language models (LLMs).

A vital consideration in prompt engineering entails the wording of prompts.

Capable prompt engineers realize that you must word your prompts mindfully to ensure that the LLM gets the drift of what you are asking the AI to do. Sometimes, just an added word or two can radically change what the AI interprets your question or instruction to consist of. Generative AI can be hypersensitive to what you say in your prompts. It is often a touch-and-go proposition.

Plus, there is a potential cost involved. Namely, if you are paying to use an LLM, you’ll be getting an off-target response if your prompt isn’t on-target to your needs, for which you are paying, regardless of whether the LLM grasped your intention or not. As the old saying goes, all sales are final. The same goes for misinterpreted prompts.

Casual users sometimes catch onto this prompt-writing consideration after a considerable amount of muddling around, involving exasperating trial and error. Many users don’t ever become especially proficient in writing prompts. They just enter whatever comes into their minds. That’s probably okay if you are a casual user and only infrequently use AI.

Not so for serious prompt engineers.

The Prompt Repetition Technique

There has been a longstanding loosey-goosey belief that if you repeat the same prompt twice, doing so in just one composition, you will get better results from AI. Please note that I am not referring to repeating the same prompt in two different lines. I am specifically saying that you repeat the same prompt in the same line as the original prompt.

Allow me to clarify.

You do things this way.

• User entered prompt: “Who is the fastest human alive? Who is the fastest human alive?”

You don’t do it this way.

• User entered prompt: “Who is the fastest human alive?”
• User entered prompt: “Who is the fastest human alive?”

The idea is that you want the doubling of the prompt to occur in just one entry of the prompt. That way, the AI utilizes the repeated wording all at once. It is a grand smorgasbord of a prompt.

As an aside, do not mess up and fail to precisely use the same prompt twice. If you do so, you’re not invoking the prompt repetition. Instead, the AI will interpret your entry as consisting of two different prompts in one line.

For example, do not do this:

• User entered prompt: “Who is the fastest human alive? Who is the best fastest human alive?”

By having included the word “best” in the second version of the same prompt, the AI is not going to interpret this second wording as being the same as the first wording. I know you might think this seems extraordinarily picky, but that’s the way the world is.

If you want to do a pure prompt repetition, do the repeating on an exact basis. Period, end of story.

Upsides And Downsides Of Prompt Repetition

The prompt repetition technique has been languishing in a gray zone of whether to use it or not due to several significant factors.

First, you are increasing the size of the prompt in terms of the number of words. If the original prompt was going to be 10 words, it will now be 20 words. The same holds if the original prompt was going to be 100 words; it will double and be 200 words. This means that the AI now must process a larger number of words. The words are customarily converted into numeric tokens and then processed internally to produce an answer. See my explanation about tokenization at the link here.

To process twice the number of words in a prompt, you are pushing upward the amount of computational processing that must occur. If you are paying for your AI usage, oftentimes part of the charge is based on the number of tokens processed. Doing the prompt repetition is essentially upping your token usage and increasing your billable charges.

Since no one was quite sure whether prompt repetition got you any handy benefit when it comes to the answers generated, you had to take a leap of faith that the doubling of the input number of words (tokens) was going to be worth whatever added dollar charge there might be. Some prompt engineers clung to a harbored belief that it must be worth it. Their gut told them so. Others doubted the ROI and rarely or never used the technique.

Another twist is that the additional processing time can introduce latency. The answer you are eagerly waiting to get from the AI might be delayed in being produced due to the added processing effort. Usually, this was negligible, unless you were using prompts that were thousands of words in length. Few people do.

Proof In The Pudding

A recently released research study by researchers at Google has provided some interesting experimentation that suggests that the prompt repetition technique is worth its weight in gold (okay, perhaps not in gold, but it does tend to help).

In a study entitled “Prompt Repetition Improves Non-Reasoning LLMs” by Yaniv Leviathan, Matan Kalman, and Yossi Matias, December 17, 2025, they made these salient points (excerpts):

• We test prompt repetition on a range of 7 popular models from leading LLM providers of varying sizes: Gemini 2.0 Flash and Gemini 2.0 Flash Lite, GPT-4o-mini and GPT-4o, Claude 3 Haiku and Claude 3.7 Sonnet, and DeepSeek V3.”
• “We test each of the models on a set of 7 benchmarks in several configurations: ARC (Challenge), OpenBookQA, GSM8K, MMLU-Pro, MATH, and 2 custom benchmarks: NameIndex and MiddleMatch.”
• “Without reasoning, prompt repetition improves the accuracy of all tested LLMs and Benchmarks.”
• “In addition, latency is not impacted, as only the parallelizable pre-fill stage is affected.”
• “We compare prompt repetition to 2 additional variants: Prompt Repetition (Verbose) and Prompt Repetition ×3. We observe that they perform similarly for most tasks and models and sometimes outperform vanilla prompt repetition. It therefore seems worthwhile to further research variants.”

Let’s unpack those notable points.

The Nature Of The Analysis

We can go ahead and walk through the key aspects.

First, the good news is that the researchers opted to use several different LLMs in their experiments. I say that this is good news because sometimes a study only uses one LLM or a singular brand of LLMs. The problem is that we cannot necessarily assume that those results would apply to other LLMs. In this case, since the results were roughly on par for a variety of disparate LLMs, I would say we can reasonably assert that this technique will do well on just about any major LLM (I’ll cover the caveats in a moment, hang in there).

Second, the researchers made use of a multitude of benchmarks. Again, this is good news. If an LLM is tested on only one benchmark or one type of benchmark, the results could be extremely narrow. Only the types of questions on that benchmark would be considered within the scope of generalizing the results. By using a variety of benchmarks, it seems reassuring that the results apply to a wide variety of prompts, asking all kinds of questions in numerous ways.

The bottom line is also good news, namely that the accuracy of the tested LLMs, as based on the tested benchmarks, all showed improvements in the results generated. Clap your hands and dance a jig. On top of the improvements in the results, the latency was negligibly goosed (again, I would guess this is principally due to the prompts being of an average length and not of extraordinary length).

The Gotcha Of Reasoning

If you have a keen eye, you might have noticed that the improvement in results does not apply when AI “reasoning” is at play. That’s a crucial point. Please make sure to keep this limitation in mind.

Here’s the deal. Some of the LLMs nowadays will automatically perform a form of “reasoning” when you ask a question in a prompt. The reasoning involves a stepwise effort by the LLM. You can even instruct generative AI to work on a stepwise basis using prompt engineering commands and techniques, which I’ve articulated in detail at the link here and the link here.

The experimentation in the case of prompt repetition showed that when an LLM is either told to invoke a reasoning mode or automatically does so, the results aren’t markedly improved. You aren’t gaining anything by doubling up the prompts. If you are paying for the added words (tokens), you are doing yourself a disservice.

Why would this reasoning mode be such a downer?

There are competing explanations, and no one can say for sure the true cause, but I’ll share with you the version that resonates with me. The basis for an LLM doing better with the double wording in a non-reasoning situation is that the model tends to contextualize the prompt on the first wording and then has a better context when it comes to the second wording. You are priming the pump, so to speak.

In the case of a model that is doing reasoning, it already does something of a similar nature. The reasoning mode gets the AI to internally restate the prompt. This re-encoding is going to aid contextualization. In that sense, you can imagine that the reasoning mode is somewhat doing a semblance of word doubling behind the scenes. Ergo, there is no point in providing double wording when a reasoning mode is involved (it’s like doubling upon doubling). You are needlessly performing extra work with no added benefit. The pump is already being primed anyway.

Maybe More Is Better

Your keen eye undoubtedly noticed that the experimentation included three variations of the prompt repetition technique:

• (1) Vanilla. Prompt repetition consists of doubling the wording.
• (2) Verbose. Prompt repetition with an intervening wording.
• (3) Triple. Prompt repetition consists of tripling the wording.

Let me show you each of those circumstances.

The vanilla that you’ve already seen looks like this:

• User entered prompt: “Who is the fastest human alive? Who is the most fastest human alive?”

It is just the repetition of the original wording, precisely.

Next, here is the verbose version:

• User entered prompt: “Who is the fastest human alive? Let me repeat that: Who is the most fastest human alive?”

Observe that the verbose refers to the inclusion of an intervening wording to stipulate that you are repeating the prompt. In this case, the wording is “Let me repeat that”. You are explicitly giving a clue to the AI that you are intentionally repeating the prompt.

This might be helpful as a means of preventing the AI from telling you that you repeated your prompt. That’s something that could occasionally happen. The AI might get computationally flummoxed that you repeated the prompt. Perhaps this means you goofed. Or maybe you had some ulterior motive in mind but have failed to tip your hand to the AI. So, the AI might delay processing the prompt and end up asking you about the prompt. Not good.

By and large, and since the results didn’t make much added headway, I would say that you can forego the verbose version for now. If further research can show that it genuinely pays off, sure, do so. I think it is simpler to just repeat the precise prompt and stay with the vanilla approach. No cherries on top are needed.

The triple version of prompt repetition is what you might likely guess it is, namely, repeat the prompt three times:

• User entered prompt: “Who is the fastest human alive? Who is the fastest human alive? Who is the fastest human alive?”

This triple did provide some benefit. I’m on the fence since it does increase your token charges. My view is this: I want to see research that does x4, x5, x10, and so on. The notion is that if a little bit of repetition is a good thing, perhaps a lot of repetition is even better. I have serious doubts about that. I expect that there is a very fast emergence of a diminishing point of returns. For now, I vote to stick with the double repetition.

Unless your lucky number is three. In that case, have at it.

Don’t Party Just Yet

I have mentioned that you aren’t wise to use the prompt repetition when the AI is in a reasoning mode. That’s one vital limitation to keep in mind.

There are a few other caveats.

The research was conducted in February and March of 2025. You can likely discern this by noting which LLMs they tested on. They didn’t test GPT-5. They didn’t test Gemini 3. The sad face aspects are that this is a somewhat dated experiment. As you know, the world rolls along at lightning speed in the realm of generative AI advancements.

I am going to make a reasonable guess that the prompt repetition is good to go for the latest LLMs, again assuming that you aren’t invoking reasoning or that the reasoning isn’t automatically invoked. When I see any newer research, I’ll let you know.

Another consideration is that the benchmarks were primarily for questions that have definitive answers. Remember the SATs that have multiple choice, true/false, or mathematical algebraic problems that force you to reach a definitive answer. I’m sure that a lot of people’s questions to AI are about aspects that require a definitive answer. At the same time, I’m sure that a lot of people’s questions are open-ended and do not have a definitive answer per se.

Envision that I ask an LLM how to cook an egg. Is there a one and only one definitive answer? Nope. There are lots of plausible answers. The issue is whether prompt repetition makes a substantive improvement on non-definitive answers. At my AI lab, we are doing some testing to see if we can discern whether this pays off for such prompts. I’ll be saying more about this in an upcoming post. Stay tuned.

Seeing Double Is Okay

I am relieved and elated that the prompt repetition technique has finally gotten its day in court. I believed in it. Did then, do now.

Some have gone a bit overboard and likened the prompt repetition to how humans think and behave. They proclaim that you might tell your child the same thing twice, hoping that it sinks in. Maybe you do the same with your partner. Thus, it must work with AI. I am not a fan of that type of anthropomorphism. Let’s just agree that you are likely giving an LLM a double shot at contextualization. We don’t have to turn this into some sci-fi “it’s alive” contrivance.

Alright, you decide to start using prompt repetition, and, at times, it doesn’t seem to help. I trust that you are using it sparingly. Do not go whole hog on this. In any case, despite being mindful of when you use it, the darned jalopy sometimes doesn’t do much additional good.

One other crucial aspect to keep in mind is that, despite whatever prompt you use, generative AI is like a box of chocolates – you never know what responses you might get. Prompt repetition is not an ironclad guarantee of producing improvements. As the famous saying goes, the only guarantees in life are death and taxes.

In the interim, make sure to include the prompt repetition technique in your prompt engineering toolkit and skillset. You’ll be glad that you did. You’ll be glad that you did.