An Overlooked Hack for Better LLM Results

Have you ever asked an LLM a question, changed the wording a few times, and realized the answer was wrong? If you’ve worked with tools like ChatGPT or Gemini, you may have rewritten information, added more context, or used phrases like “be concise” or “think step by step” to improve results. But what if improving accuracy was as easy as copying all your information and pasting it again? That’s a concept that causes rapid replication. It may sound too simple to matter, but research shows that modeling your question twice can significantly improve the accuracy of many tasks, making it one of the easiest workouts to try.

What is Fast Replication and Why Try It?

To understand why repetition helps, we need to look at how LLMs process text. Many types of large languages ​​are trained in a causal manner. They predict tokens one by one, and each token can only pay attention to the tokens that came before it. This means that the order of information in your information can affect the understanding of the model.

Fast replication helps reduce this ordering effect. If you repeat the information, every token gets another chance to deal with all the relevant information. Instead of seeing the context once, the model correctly processes it twice during input (pre-filling).

Importantly, this happens before the model starts generating feedback. The output format does not change, and the model does not generate additional tokens. You simply improve how the model processes the input.

Also Read: Prompt Engineering Guide 2026

Quick Replication in Action

The study tested rapid iteration across 7 different tasks using 7 LLMs. These were not small test models. They include widely used models such as Gemini, GPT-4o, Claude, and DeepSeek, accessed through their official APIs. The seven tasks included:

Five common benchmarks:

• ARC (scientific reasoning questions)
• OpenBookQA
• GSM8K (mathematical word problems)
• MMLU-Pro (multi-domain knowledge)
• STATISTICS

Two custom designed functions:

Custom tasks were specifically designed to test how well the models handle structured and spatial information.

For each task, the researchers compared two setups:

1. The first step
2. The same information is duplicated

Nothing else was changed. The output format remained the same. The model was not configured properly. The only difference was that the input was a duplicate.

Then they measured:

• Accuracy
• Output length
• The delay

AI Benchmarks Guide Covering Everything MMLU, HumanEval, and More

Result of Rapid Repetition Test

In all seventy comparisons involving different models and benchmarks, fast iteration improved accuracy forty-seven times. It did not significantly reduce performance. Improvements were particularly evident in multiple-choice formats and structured tasks where the model required careful tracking of spatial information.

Example from Paper: The NameIndex function

In the NameIndex task, the model is given a list of 50 names and asked a specific question: “What is the 25th word?” The work does not require thinking or interpretation. It only requires accurate location tracking within the range.

In the first case, the performance was low. For example, Gemini 2.0 Flash Lite achieved an accuracy of 21.33%. After applying fast iteration, the accuracy increased to 97.33%. This is a huge improvement in reliability.

Indexing requires that the model correctly document the sequence and location. If the prompt appears once, the model processes the list and the query in one pass. Some hierarchical relationships are likely to be strengthened. If the full list and query are repeated, the model successfully executes twice before responding. This reinforces its internal representation of ordering.

But What About Delay Costs and Penalties?

Whenever we improve accuracy, the next question is obvious: At what cost? Surprisingly, almost nothing.

These figures compare:

• Accuracy
• Average response length
• Length of media response
• The delay

Key findings:

• Fast iteration does not increase the length of the output token.
• The model does not produce long answers.
• The delay also remains almost the same, except in very long cases (especially with Anthropic models), where the filling phase takes longer.

This is important for production systems.

Unlike chain-of-sight information, which increases token production and cost, rapid iteration shifts the computation to a complementary, parallel phase.

In real-world applications:

• Your cost per request does not increase
• The format of your answer remains the same
• Your analytical logic below will remain the same

This makes it easier to deploy.

When Does Fast Replication Work Best?

A quick iteration doesn’t magically fix all problems. Research shows that it works best for non-thinking tasks, especially when the model has to carefully process structured or ordered information.

It usually works best in situations like:

• Answers to multiple choice questions
• Tasks that involve a long context followed by a short question
• List of problems or problems to retrieve
• Scheduled data release
• Classification functions with clearly defined labels

The improvement is particularly noticeable when the model has to accurately track positions or relationships between programmed inputs. Repeating the command strengthens that relationship.

However, when implicit reasoning is enabled, such as telling the model to “think step by step,” the benefits are small. In those cases, the model often repeats or reprocesses parts of the question during the consultation anyway. Repetition still doesn’t hurt performance, but the improvements are often neutral rather than spectacular.

The key takeaway is simple. If your job doesn’t require long thought processes, quick iteration is probably worth checking out.

How to Use Repetition Faster When Practicing

Implementation is straightforward. You don’t need special tools or model changes. You simply iterate over the input string before sending it to the model.

Instead of sending:

prompt = query

You send:

prompt = query + "n" + query

That’s all the change.

There are a few considerations that come into play. First, make sure that the length of your input does not exceed the model’s context window. Doubling too long information may push you to the limit. Second, check for a change in your specific job. Although research shows consistent benefits, each production system has its own characteristics.

The advantage of this method is that nothing else in your system needs to change. Your output format remains the same. Your analysis logic remains the same. Your test pipeline remains the same. This makes it easy to experiment without risk.

Fast Replication vs. Thought Stimulation Series

It is important to understand how rapid repetition differs from chain of thought discussion.

Chain-of-Caught prompts encourage the model to explain its reasoning step by step. This often improves the performance of calculations and logic-heavy operations, but increases output length and token consumption. It also changes the structure of the response.

A quick iteration makes an exception. It does not change the way out. It does not ask the model to speak out loud. Instead, it reinforces how input is coded before production begins.

In experiments, when thinking instructions were used, repetition produced neutral effects. That makes sense. If the model is already updating the query during its reasoning process, repeating the information adds little new information.

For tasks that require detailed thinking, chain thinking may be useful. For structured or classification-style tasks where you need short answers, rapid iteration provides easy and cheap improvements.

Key Takeaways for Developers

If you’re building powerful LLM systems, here’s what this study says:

• Check the repetition of information in non-thinking tasks.
• Prioritize structured or position-sensitive workflows.
• Measure the accuracy before and after the change.
• Monitor the context length to avoid hitting token limits.

Because this method does not change output formatting or significantly increase latency, it is safe to test in staging areas. In many cases, it can improve durability without structural changes or repairs.

In production systems where small improvements in accuracy translate into measurable business impact, even a few percentage points can be significant. In some organized activities, the benefits are much greater.

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The conclusion

Agile engineering often feels like trial and error. We modify the sentence, add constraints, and test different instructions. The idea that repeating all the information can improve accuracy may sound simplistic, but experimental evidence suggests otherwise.

Across multiple models and seven different tasks, fast iteration consistently improved performance without increasing output length or significantly affecting latency. The method is easy to use, does not require retraining, and does not change the formatting of the response.

Try it yourself and let me know your take in the comments section.

Get all the details here: Fast Iteration Improves Research Paper for Non-Thinking LLMs