Interacting with AI via prompts—i.e., text instructions—seems simple, but it hides plenty of surprises. In recent years (2023–2025), researchers have intensively analyzed how the form and style of our queries affect the responses of large language models (LLMs). It turns out that some popular beliefs about the “art of asking questions” need revisiting. Below are five surprising, research-backed truths about prompts. These findings give AI conversations a more scientific edge—and might change how you formulate instructions.
1. Simple vs. complex prompts—more isn’t always better
Intuitively, one might think that the more detailed and elaborate the prompt, the better the answer. Reality is more nuanced. Early experiments showed that adding an instruction like “Think step by step” can trigger a model’s reasoning process and improve performance on logic or math problems—this is Chain-of-Thought (CoT) prompting. As early as 2022, it was shown that a few worked examples prompted step by step enabled PaLM 540B to achieve state-of-the-art on GSM8K, even surpassing a fine-tuned GPT-3 with a verifier [1]. This illustrates the potential of complex prompts: a well-crafted instruction can “unlock” latent reasoning abilities.
More recent studies suggest tempering universal enthusiasm for complex prompts. More complicated prompts don’t always yield better results. A 2025 report (Wharton GAIL) indicates that for newer, “reasoning” LLMs, the gains from explicitly forcing reasoning can be marginal, while time/token costs rise; effects vary by model and task [2]. In other words, overly intricate prompts can introduce unnecessary steps and mislead the model—while larger models may already plan internally without extra instructions.
Additionally, in one experimental survey Gemma 2 9B maintained similar effectiveness whether the prompt was simple or multitask—suggesting stability with respect to prompt type in certain tasks [10]. The surprising truth: the simplest approach is often as effective as elaborate prose. Clarity is key—if the task doesn’t require a complex structure, a simple, precise prompt may be best.
2. Prompt tone and politeness matter—but not always as you expect
You might expect politeness to aid collaboration with AI. Recent findings are mixed. A 2025 study on GPT-4o reports a small advantage for terse, “brusque” instructions over very polite ones (e.g., ~80.8% vs. ~84.8% accuracy on multiple-choice questions) [4]. However, this is preliminary, on a single model and a modest question set. Moreover, an earlier cross-lingual study (2024) showed that impolite prompts often degrade performance, with the optimum depending on language and model [3].
Practical takeaway: experiment with form (terse/direct vs. polite/expanded), but don’t generalize that “being impolite” always helps. Changing tone can nudge the model toward more concise answers, yet it can also harm quality—especially across languages.
3. Structure matters: long prompts benefit from being split and “sculpted”
The more complex the task, the easier it is to produce a “wall of text.” Structuring the instruction (sections, steps, plan → execution) usually helps the model. A good example is SCULPT—a method that treats a long prompt as a tree and refines it iteratively (Critic + Actor), improving performance and robustness to small perturbations. SCULPT was presented at ACL 2025 [5].
In practice, Step-Back Prompting also works well—first ask the model to identify the problem type and governing principles/plan (abstraction), then generate the solution. In the authors’ tests, the technique improved results by +7 pp on MMLU (Physics), +11 pp on MMLU (Chemistry), and +27 pp on TimeQA (PaLM-2L) [7].
4. Fine-tuning—teach the model your style instead of repeating it in prompts
Hand-tuning prompts has limits. If you need consistent style/format across an organization, consider fine-tuning—updating model weights on your data. In August 2023, OpenAI stated that a fine-tuned GPT-3.5-Turbo can, on narrow tasks, match base GPT-4 (a vendor claim; always verify locally) [8]. It’s useful to distinguish fine-tuning from prefix/prompt-tuning, which add “virtual” prefix tokens without changing weights (see Li & Liang, 2021) [9].
5. Small change, big effect: models are sensitive to prompt nuances
Two semantically equivalent questions can yield dramatically different results. On the RobustAlpacaEval benchmark, spreads of ~45 percentage points between the best and worst paraphrase for the same model were observed; in extreme cases, the worst variant of Llama-2-70B-chat dropped to 9.38% [6]. New techniques (e.g., instruction self-denoising) improve robustness to typos and small perturbations [11], but the core issue—semantic instability—remains. Practice: favor clarity and test multiple prompt variants.
Conclusion
The era of prompt engineering is evolving rapidly. We’ve seen cases where simplicity beats over-engineering, and cases where an unusual factor (like tone) shifts outcomes. We’ve learned that prompt structure can be optimized much like code, and when that’s not enough—you can change the model itself via fine-tuning. All this leads to one takeaway: talking to AI is a skill that blends creativity with an understanding of how the model works. With the above truths in mind, you can better extract what you need—be it precise answers or a preferred style.
Practical tips (at a glance)
- Start with a simple prompt; add structure (steps, sections, examples) only if results are weak.
- Test tone and style (terse/direct vs. polite/expanded)—effects depend on model and language.
- Decompose complex tasks into steps (plan → execution) and split long context into sections; consider SCULPT/Step-Back.
- Standardize output formats (e.g., “Return JSON with fields …”)—this improves repeatability.
- Measure cost/tokens: CoT and plan-and-solve increase latency and cost—use them where they truly improve accuracy.
- If you need consistent style at organizational scale, consider fine-tuning/prefix-tuning rather than long “super-prompts.”
References / Bibliography
- Wei, J. et al. (2022), Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. (PaLM 540B; SOTA on GSM8K). arXiv:2201.11903.
- Meincke, L., Mollick, E., Mollick, L., Shapiro, D. (2025), Prompting Science Report 2: The Decreasing Value of Chain-of-Thought in Prompting. (Diminishing CoT gains; time/token cost). Wharton GAIL, arXiv:2506.07142.
- Yin, Z. et al. (2024), Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance. (Politeness: effects depend on language/model). ACL Anthology, arXiv:2402.14531.
- Dobariya, O., Kumar, A. (2025), Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy. (GPT-4o: ~80.8% vs. ~84.8%; preliminary, one model). arXiv:2510.04950.
- Kumar, S. et al. (2024/2025), SCULPT: Systematic Tuning of Long Prompts. (ACL 2025; robustness to perturbations). ACL 2025, arXiv:2410.20788.
- Cao, B. et al. (2024), On the Worst Prompt Performance of Large Language Models. (RobustAlpacaEval; spreads up to ~45 pp; minima ~9.38%). arXiv:2406.10248, NeurIPS 2024 (PDF).
- Zheng, H.S. et al. (2023), Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models. (Step-Back: +7/+11 pp MMLU; +27 pp TimeQA on PaLM-2L). arXiv:2310.06117.
- OpenAI (2023), GPT-3.5-Turbo fine-tuning and API updates. (Claim: fine-tuned 3.5 can match base GPT-4 on narrow tasks). openai.com.
- Li, X.L., Liang, P. (2021), Prefix-Tuning: Optimizing Continuous Prompts for Generation. (“Virtual” prefixes; no weight updates). arXiv:2101.00190, ACL 2021.
- Gozzi, M., Di Maio, F. (2024), Comparative Analysis of Prompt Strategies for Large Language Models: Single-Task vs. Multitask Prompts. (Gemma 2 9B: small differences across prompt types). Electronics 13(23):4712.
- Agrawal, R. et al. (2025), Enhancing LLM Robustness to Perturbed Instructions. (Iterative instruction self-denoising). PDF.
Note: effects depend on the model, language, and benchmark; test prompt variants locally on your own tasks.