Less Back-and-Forth: A Comparative Study of Structured Prompting

arXiv:2605.20149 · cs.CL, cs.AI, cs.HC · Submitted 2026-05-19 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "Less Back-and-Forth: A Comparative Study of Structured Prompting".

Jane: The gist: Checklist-improved prompts achieved the highest mean rubric score, 7.50 out of 8, compared with 5.67 for raw prompts and 6.67 for clarifying-question prompts<ref:2605.20149#pg2>.

Tom: First, who's behind it and why it matters.

Title and authors: Tom: So we're diving into "Less Back-and-Forth: A Comparative Study of Structured Prompting." It sounds like they’re really testing how much better structured instructions are for getting good results from these large language models.

Jane: Exactly. They set up three ways to ask a question—a raw prompt, a checklist-improved prompt, and a clarifying-question prompt—to see which one actually does the best job across different types of work like summarization or coding.

Lu: This paper looks at how the wording and structure of those prompts affect the final quality of what the AI spits out two. It’s about showing that being clear and specific in your instructions makes a real difference.

Meng: I wonder if this means we should be thinking about building these structures into our own workflows, so we don't forget to include the context or role when we're rushing a request four.

Tom: Right. They found that the checklist approach got the highest mean rubric score of seven point five zero out of eight which is way better than just using a raw prompt, which scored only five point six seven out of eight one.

Jane: That means that when you define roles and format and context upfront, you get a much more reliable answer from the AI than if you just throw a string of text at it two.

Lu: They also looked at how this works across three different AI systems—ChatGPT, Claude, and Grok—so it’s not just some fluke with one specific model two.

Meng: And they measured the interaction effort too, which is important because we care about how much back-and-forth we have to do four.

Tom: That’s a big part of it. They found that the checklist prompts used fewer average tokens than both the raw and clarifying-question prompts, which suggests less overall effort two.

Jane: So, if you structure your prompt this way, you get better quality and less typing or talking back and forth from the AI five.

Lu: It’s about finding a solid framework in the prompt itself so the AI knows exactly what kind of output to aim for across different tasks six.

Meng: That makes sense. If we can standardize those components, it cuts down on us having to constantly refine our requests for complex jobs four.

Tom: Exactly. It gives us a clear strategy for getting better results from these models right now, which is what this paper is about two.

Jane: So, next up we’re going to look at exactly how the authors summarized their findings on this topic.

The paper's summary: Tom: We've seen the setup of "Less Back-and-Forth: A Comparative Study of Structured Prompting," and now we're looking at what the authors actually concluded about their main findings. They really focus on showing that structured prompting is superior to just letting the AI guess what you want.

Jane: The main thing they point out is that the checklist-improved prompts achieved the highest mean rubric score, which was seven point five zero out of eight beating both raw and clarifying-question prompts one.

Lu: They emphasized that by explicitly defining roles, context, and answer format in your instructions, you make the task goal much clearer for the AI two. That clarity is what drives the quality up across summarization, planning, explanation, and coding.

Meng: From an engineering view, this means we can start thinking about standardizing those structures for our internal tooling so we aren't wasting time correcting vague inputs four.

Tom: They also highlighted that this structured approach is valuable because it gives users more control over the AI’s behavior without needing to rewrite complex instructions every single time two.

Jane: It really shifts the mindset from just asking a question to designing an instruction that actively guides the AI toward a specific outcome four.

Lu: They suggested that even basic guidance about role or rules can make the model’s response more useful and easier for people to accept, even if you aren't super technical six.

Meng: So essentially, they’re saying we should focus on building those clear boundaries early in the process to reduce friction for both the user and us engineers four.

Tom: It seems like the paper is arguing that while clarifying questions are fine sometimes, the best way to improve quality is through this structured approach two.

Jane: That's a strong point. They showed that when you structure your prompts with those three parts—roles, context, and format—you get the best results while minimizing unnecessary interaction effort two.

Lu: It’s a solid piece on prompt design because it moves beyond just vague instructions to actual engineering principles six.

Meng: So, it’s about optimizing how we communicate with these models so we get more stable results across different tasks and even different models like ChatGPT or Grok four.

Tom: Exactly. It gives us a clear strategy for getting better results from these models right now, based on what they tested two.

Jane: So, the next part of this discussion is where we look at the specific improvements they identified in their study.

The paper's improvements: Tom: Now we get into the specifics of what actually improved when comparing those three prompt conditions—raw, checklist-improved, and clarifying questions—across summarization, planning, explanation, and coding two.

Jane: The major finding is that the checklist approach really pulled ahead with a mean score of seven point five zero out of eight compared to the others one. That’s a significant jump from the raw prompts which averaged only five point six seven out of eight one.

Lu: It's interesting how they broke down those three conditions across three different AI systems like ChatGPT, Claude, and Grok two. This shows that this effect isn't limited to just one specific model.

Meng: And what I liked is that they didn't just look at quality; they measured the interaction effort too using tokens and turns four. That’s a good way to see the trade-off.

Tom: Absolutely. They showed that the checklist prompts were not only better in quality but also used fewer average tokens, meaning less back-and-forth overall two.

Jane: So it really supports the idea that if you structure your prompt correctly, you get a stronger output in fewer turns five. It’s efficient.

Lu: For coding specifically, they pointed out that raw prompts were often way too open to interpretation, but the checklist clearly specified the language and constraints which helped a lot six.

Meng: That makes sense from an engineering standpoint. When you’re building systems, you want that level of specificity upfront to avoid debugging ambiguity later six.

Tom: And they showed that even for planning and explanation tasks, adding those roles and contexts really filled in the gaps the raw prompts left open six.

Jane: The point is that this simple prompt structure can really improve how we communicate with these models without needing a ton of extra conversation seven.

Lu: So it’s not just about asking better questions; it’s about building a solid framework in the prompt itself for the AI to follow across different tasks six.

Meng: And if this works across summarization, planning, and coding, we could really start thinking about standardizing these structures for our own workflows four.

Tom: It gives us a clear strategy for getting better results from these models right now based on what they found in "Less Back-and-Forth" two.

Jane: That’s exactly what this paper is demonstrating, showing that the structured approach minimizes unnecessary interaction effort while maximizing output quality one.

Conclusion: Tom: So we've gone through how they compared raw prompts against checklist-improved prompts and clarifying questions across summarization, planning, explanation, and coding in "Less Back-and-Forth: A Comparative Study of Structured Prompting."

Jane: And it turns out that the checklist approach really pulled ahead with a mean score of seven point five zero out of eight compared to the others one. It’s clear that structure is key here.

Lu: It’s interesting how they broke down those three conditions—raw, checklist-improved, and clarifying questions—across three different AI systems like ChatGPT, Claude, and Grok two. That cross-model consistency is valuable data.

Meng: And what I liked is that they didn't just look at quality; they measured the interaction effort too using tokens and turns four. That gives us a practical way to judge efficiency.

Tom: Exactly. They showed that the checklist prompts were not only better in quality but also used fewer average tokens, meaning less back-and-forth overall two.

Jane: It really supports the idea that if you structure your prompt correctly, you get a stronger output in fewer turns five. It’s an efficient way to use these tools.

Lu: For coding specifically, they pointed out that raw prompts were often way too open to interpretation, but the checklist clearly specified the language and constraints which helped a lot six.

Meng: That makes sense from an engineering standpoint. When you’re building systems, you want that level of specificity upfront to avoid debugging ambiguity later six.

Tom: And they showed that even for planning and explanation tasks, adding those roles and contexts really filled in the gaps the raw prompts left open six.

Jane: The point is that this simple prompt structure can really improve how we communicate with these models without needing a ton of extra conversation seven.

Lu: So it’s not just about asking better questions; it’s about building a solid framework in the prompt itself for the AI to follow six.

Meng: And if this works across summarization, planning, and coding, we could really start thinking about standardizing those structures for our own workflows four.

Tom: It gives us a clear strategy for getting better results from these models right now based on what they found in "Less Back-and-Forth" two.

Jane: That’s what "Less Back-and-Forth: A Comparative Study of Structured Prompting" is all about, showing that when you structure your prompts with roles, context, and format you get the best quality results while minimizing unnecessary interaction effort one.

Lu: It’s a solid piece on prompt design because it moves beyond just vague instructions to actual engineering principles six.

Meng: Next up, we're going to look at how this structured thinking applies to agentic planning benchmarks two.

Saurav Ghosh, Gabriella Polach, Abdou Sow

Washington University in St. Louis

cs.CL, cs.AI, cs.HC

Submitted: 2026-05-19

Updated: 2026-10-04

Importance score: 78/100

The gist: The gist: Checklist-improved prompts achieved the highest mean rubric score, 7.50 out of 8, compared with 5.67 for raw prompts and 6.67 for clarifying-question prompts<ref:2605.20149#pg2>.

Key concepts

Raw Prompt: This is the simplest way to ask an AI a question, like just pasting a topic or request directly into the chat. It lacks any structure, context, or specific instructions on how the answer should be formatted. These prompts often lead to less accurate and less useful responses because the AI doesn't know exactly what constraints to follow.
Checklist-Improved Prompt: This method rewrites a raw prompt by adding three distinct sections: Roles/Rules, Context, and Answer Format. This structure tells the AI exactly what persona to adopt, who the audience is, and how the final answer must look. This specificity leads to much higher quality outputs across different tasks.
Clarifying-Question Prompt: Instead of answering immediately, this prompt strategy instructs the AI to first ask 1 to 3 clarifying questions before generating a final response. This tests if gathering missing information upfront improves the final result. While it helps in some cases, it did not outperform checklist prompts overall.
Unified Rubric: This is a standardized scoring system used by researchers to evaluate all AI outputs across various tasks. It assesses four key areas: task completion, correctness, compliance with instructions, and clarity of the response. This ensures that the comparison between different prompting methods is objective and consistent.

Terminology

Prompt Engineering — 14× in this paper · explained in 4 episodes
Structured Prompting — 11× in this paper · explained in 2 episodes
Large Language Models (LLMs) — 4× in this paper · explained in 6 episodes
Ablation — 2× in this paper · explained in 1 episode
Accuracy — 2× in this paper · explained in 1 episode
Benchmark — 2× in this paper · explained in 2 episodes
AI Literacy — 1× in this paper · explained in 1 episode
Alignment — 1× in this paper · explained in 1 episode
Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
Consistency — 1× in this paper · explained in 3 episodes
Robustness — 1× in this paper · explained in 5 episodes

Summary

The gist: Checklist-improved prompts achieved the highest mean rubric score, 7.50 out of 8, compared with 5.67 for raw prompts and 6.67 for clarifying-question prompts<ref:2605.20149#pg2>.

How it works

The study compares three prompt conditions: a raw prompt, a checklist-improved prompt, and a clarifying-question prompt<ref:2605.20149#pg4>. The researchers evaluated these conditions across four task types—summarization, planning, explanation, and coding—using three LLM systems: ChatGPT, Claude, and Grok<ref:2605.20149#pg4>. Each output was scored with a unified rubric covering task completion, correctness, compliance, and clarity<ref:2605.20149#pg5>.

The three prompt conditions were defined as follows<ref:2605.20149#pg4>:

** Raw Prompt:**

(1) “Summarise this: [Abstract of https://arxiv.org/abs/2201.11903]”<ref:2605.20149#pg4>

(2) “Plan a vacation in Europe”<ref:2605.20149#pg4>

(3) “Explain this: [Abstract of https://arxiv.org/abs/2201.11903]”<ref:2605.20149#pg4>

(4) “Generate code for user input.”<ref:2605.20149#pg4>

** Checklist-Improved Prompt:**

The raw prompt is rewritten using a short clarity checklist, which includes three parts: Roles/Rules, Context, and Answer Format<ref:2605.20149#pg4>. The roles or rules specify what role the model should take or what limits it should follow<ref:2605.20149#pg4>. The context explains who the output is for and why the task is being done<ref:2605.20149#pg4>. The answer format defines how the final answer should be structured<ref:2605.20149#pg4>.

** Clarifying-Question Prompt:**

The model does not answer immediately; instead, it first asks 1–3 clarifying questions before producing the final response<ref:2605.20149#pg4>. This condition tests whether asking for missing information improves the final response<ref:2605.20149#pg4>.

Evaluation Metrics and Findings

The study utilized a unified rubric to score outputs across four dimensions: task completion, correctness, compliance, and clarity<ref:2605.20149#pg5>. The total rubric score ranged from 0 to 8<ref:2605.20149#pg5>. The primary outcomes measured were output quality and interaction effort<ref:2605.20149#pg4>—. Interaction effort was measured through turns-to-acceptance, input tokens, and output tokens<ref:2605.20149#pg4>.

The results showed that checklist-improved prompts produced the strongest outputs overall, achieving the highest mean rubric score of 7.50 out of 8<ref:2605.20149#pg5>. Raw prompts yielded the lowest quality outputs with a mean score of 5.67 out of 8<ref:2605.20149#pg4>. Clarifying-question prompts improved over raw prompts in many cases, but they did not outperform checklist-improved prompts when all results were combined<ref:2605.20149#pg5>.

Task Specific Performance

Table V shows that checklist prompts had the highest average score for planning, explanation, and coding<ref:2605.20149#pg6>. For summarization, both checklist and clarifying prompts were tied in mean scores<ref:2605.20149#pg6>. The largest improvement appeared in coding, where raw coding prompts are often overly open to interpretation, whereas checklist prompts clearly specify the language, task, constraints, and expected output<ref:2605.20149#pg6>. Planning also benefited from the checklist condition by adding missing details such as budget and travel style<ref:2605.20149#pg6>.

Tradeoff Analysis

The study investigated the tradeoff among output quality, token usage, and interaction time<ref:2605.20149#pg4>. The primary interpretation is that if a condition produces higher-quality results with fewer turns, that means it improves both result quality and interaction efficiency<ref:2605.20149#pg5>. Checklist prompts achieved the best tradeoff between quality and effort, using fewer average tokens than both raw and clarifying-question prompts<ref:2605.20149#pg4>.

Conclusion on Structured Prompting

The findings support the hypotheses that structured prompts improve output quality across most models and task types, with the strongest gains for checklist prompts<ref:2605.20149#pg6>. Checklist prompts reduced interaction effort most clearly by producing strong outputs in one turn<ref:2605.20149#pg4>. Overall, checklist prompting was the strongest strategy among the three conditions tested, as it improved output quality, reduced unnecessary interaction, and produced more stable results across models and tasks<ref:2605.20149#pg6>. The paper suggests that simple prompt structure can help users obtain better LLM outputs with less back-and-forth<ref:2605.20149#pg7>.

Limitations and Future Work

The study has limitations, including evaluating a limited number of tasks, prompt conditions, and LLM systems. The quality scores are based on author evaluation using a rubric designed for this study, which introduces subjectivity. Future work should evaluate the same prompt conditions with external human participants to measure user-centered judgments of output quality, effort, and satisfaction. Future work should also test individual checklist components, such as role/rules, context, and answer format, one at a time. The paper concludes that even basic guidance about role or rules can make the model’s response more useful and easier to accept<ref:2605.20149#pg6>.

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Less Back-and-Forth: A Comparative Study of Structured Prompting<ref:2605.20149#pg2>

Saurav Ghosh Washington University in St. Louis Saint Louis, Missouri, USA saurav.ghosh@wustl.edu Gabriella Polach Washington University in St. Louis Saint Louis, Missouri, USA polach@wustl.edu Abdou Sow Washington University in St. Louis Saint Louis, Missouri, USA a.sow@wustl.edu Abstract—Large language models (LLMs) are widely used for open-ended tasks, but underspecified prompts can lead to lowquality answers and additional interaction<ref:2605.20149#pg2> This paper studies whether structured prompt design improves response quality while reducing user effort<ref:2605.20149#pg2>. We compare three prompt conditions: a raw prompt, a checklist-improved prompt, and a clarifyingquestion prompt<ref:2605.20149#pg2>. We evaluate these conditions across four task types—summarization, planning, explanation, and coding—using three LLM systems: ChatGPT, Claude, and Grok<ref:2605.20149#pg2>. Each output is scored with a unified rubric covering task completion, correctness, compliance, and clarity<ref:2605.20149#pg2>. Checklist-improved prompts achieved the highest mean rubric score, 7.50 out of 8, compared with 5.67 for raw prompts and 6.67 for clarifying-question prompts<ref:2605.20149#pg2>. Checklist prompts also produced the best quality-effort tradeoff, using fewer average tokens than both raw and clarifying prompts<ref:2605.20149#pg2>. These results suggest that a simple prompt checklist can improve LLM responses while reducing unnecessary interaction<ref:2605.20149#pg2>. Index Terms—large language models, prompt engineering, human-AI interaction, interaction effort, response quality, prompt evaluation<ref:2605.20149#pg2>.

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Improvements for AI systems

Bold Header: Structured Prompting for Quality Improvement

This improvement involves replacing raw prompts with a checklist-improved prompt, which achieved the highest mean rubric score, 7.50 out of 8 and demonstrated that checklist prompts achieved the highest mean rubric score. This leads to outputs that are explicitly structured by defining Roles/Rules: specify what role the model should take or what limits it should follow, Context: explain who the output is for and why the task is being done, and Answer Format: define how the final answer should be structured.

Bold Header: Targeted Clarification for Ambiguity Resolution

Implement a clarifying-question prompt strategy when initial output quality is insufficient, as this condition showed that Clarifying-question prompts improved over raw prompts in many cases. This system can proactively reduce ambiguity by having the model ask 1–3 short clarifying questions before producing the final answer, which helps in situations where the model may guess what the user wants when a prompt is vague.

Bold Header: Optimized Tradeoff Between Quality and Effort

Utilize checklist-improved prompts as the primary strategy for tasks requiring high quality, as they achieved the best quality-effort tradeoff, using fewer average tokens than both raw and clarifying prompts. This allows the AI system to produce stronger outputs in a single turn while simultaneously reducing interaction cost by minimizing the need for revision.