Natural-language requests vs. long structured prompts: what actually improves performance?

Prompting
1

I’m curious about the difference between natural-language requests and long, highly structured prompts.

In many prompt examples, especially business-oriented ones, I often see long prompts that look like instructions or specifications:

  • “You are an expert…”
  • “Follow these conditions…”
  • “Think step by step…”
  • “Output in this format…”

But in actual use, I sometimes get better results by asking in natural language and then refining through conversation.

So I’m wondering:

Is the performance difference really about prompt length, or is it more about context density, structure, verifiability, and the ability to iterate through dialogue?

For example, a short natural-language request can work well if it clearly includes:

  • the goal
  • constraints
  • what should be checked
  • the desired output format
  • what should not be changed

On the other hand, a long prompt may become confusing if it contains too many vague, duplicated, or conflicting instructions.

How do others think about this?

Do you find long structured prompts more reliable, or do you prefer natural-language requests with iterative refinement?

As a hypothesis, I also wonder whether this may depend on the type of model.

Maybe models that tend to reach conclusions quickly work better when the user provides the goal, constraints, and expected output all at once.

On the other hand, models with stronger context retention and reasoning ability may be better suited to a more gradual style, where the user and the model build the structure together through dialogue.

In that case, the best prompting style might not be universal. It may depend on whether the model is better at processing a well-defined instruction upfront, or better at refining the task together with the user over multiple turns.

I did a bit of searching, but I couldn’t find research that directly compares natural-language requests with long prompt-style instructions.

If anyone knows of relevant research, experiments, or practical examples, I’d be happy to learn about them.

2

I prefer structured-requests, but without long queries. If I want to be more specific in something, I definitely add a comment in natural language, sometimes even quite informal peer-style like.

3

That sounds like a very practical hybrid style.

Would you mind sharing a small example of what your structured-but-not-long prompt usually looks like?

I’m especially curious about how you separate the structured part from the informal natural-language comment.

4

Short answer: you need to read the model cards and prompting guidance for each model to understand what works best.

This applies not only to OpenAI models, but also to models from other providers. Different models can respond better to different prompting styles, constraints, and levels of detail.

5

Thank you, that makes sense. I agree that model-specific guidance and model cards are important.

What I’m curious about is a slightly different angle: even for the same model, is the key factor really prompt length, or is it more about context density, structure, verifiability, and the ability to refine the task through dialogue?

For example, a concise structured request plus a natural-language comment may work better than a long instruction-style prompt, even if both are “structured.”

If you know of any guidance, studies, or practical examples that separate these factors, I’d be happy to read them.

6

I think it depends less on length itself and more on whether the prompt contains useful structure.

For exploratory or creative work, natural language can work better at first, because the conversation helps reveal what the user actually wants. A long structured prompt too early can sometimes lock the task into the wrong shape.

The same applies when building persona- or style-focused prompts. Natural language can be useful early on because the goal is often to find what kind of output best fits the user’s need: style , level of detail, boundaries, phrasing and overall response shape. That usually benefits from testing and iteration rather than starting with a rigid specification.

But when the goal is already clear, especially if consistency, constraints, compatibility or output format matter, a structured prompt becomes much more useful.

So I don’t see it as natural language versus structured prompts exactly. More like:

Natural language works well for discovery, tone, intent and iteration.

Structured prompts work well for repeatability, precision, constraints and testing.

A long prompt is only better if the extra words reduce ambiguity. If they add vague, duplicated or conflicting instructions, they may lead to a less useful or less aligned output.

7

There is no one answer for those that covers prompting in general.

One of the biggest changes for GPT 5.5:

image
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@vb noted this on the forum, and you will have to search for it.

If you go back to the GPT-3 era, asking for chain-of-thought reasoning was common and often recommended.

However, with modern reasoning models that use internal reasoning or thinking tokens, explicitly asking for chain of thought is generally ill-advised.

OpenAI has an app or web page where you can submit a prompt and it will improve the prompt, I can not find it at the moment.

While this is for the API, it is of value to read.

The reason I am not going into more detail is that this is a long, deep, and complex rabbit-hole question.

Prompting is a large topic on its own. I have not checked, but I would not be surprised if someone could get a Ph.D. for it.

8

Thank you, this distinction is very helpful.

I like the way you separated discovery, tone, intent, and iteration from repeatability, precision, constraints, and testing.

It makes me think that this may not be “natural language vs. structured prompts,” but rather “goal discovery vs. goal control.”

Natural language and iterative dialogue seem useful in the upstream phase, when we are still trying to discover the goal, clarify the intent, and find the right tone or shape.

Structured prompts seem more useful in the downstream phase, when the goal is already clear and we need consistency, constraints, compatibility, testing, or repeatable output.

So maybe these are not competing prompting styles, but tools for different stages of the workflow.

The point about long structured prompts locking the task into the wrong shape too early is especially interesting. It may happen when we apply a downstream-style specification before the upstream discovery is finished.

Thanks again. This helped me clarify the question a lot.

9

Thank you, that makes sense. I can see why this becomes a deep rabbit-hole, especially because prompting practices seem to change across model generations.

The point about chain-of-thought being common in the GPT-3 era but not generally advisable for modern reasoning models is especially helpful.

It makes me think that this question needs to be separated by model type, model generation, task stage, and whether the user is still discovering the goal or trying to control a known output.

I’ll read the OpenAI prompting guidance and prompt optimizer docs. Thanks for pointing me in that direction.

10

I try to follow the prompt guidance when convenient. But it really depends on the task I want you deliver. If it is a big task and I need to explicitly state some conditions to avoid misdirection I use the markdown format (I think for 5.5 they recommend XML format). But, if it’s small tasks and I think it doesn’t need too much constraints, because the direction seems obvious, I write simple prompts. But, it really depends on the task and how is the workflow in the thread. For example, probably with /goal a simpler prompts is enough as the documentation already states.

11

Here is another side of the coin, similar to the use of chain-of-thought.

At present, I can think of only one prompt I use that has survived unmodified since the GPT-3 models:

polish this text

It was one of the first prompts I used. It went through about 20 revisions, at one point growing to more than a page, before finally being reduced to those three words for use with GPT-3.

Even today, meaning within the last six hours, I have used that single prompt more than thirty times.

On the other hand, I have another prompt that I have been using for the last several days which is allowed to modify itself. It is stored as a Markdown file and has simply evolved over time. I cannot even fully explain how or why it has become what it is, but it has turned into a remarkable prompt that helps with code development for one specific project.

Also, it helps to think of prompts not as a fixed set of instructions, but as part of a conversation.

One of the most effective additions I have used in prompts involving code changes is this simple line:

Ask questions as needed?

It is not as necessary now, since AI IDE tools such as Codex and Claude Code often ask clarifying questions on their own when needed. Still, I occasionally add it to coax the AI into asking questions so I can better understand how it is approaching the task.

12

Just in case I can help a little on my end:

I talk to ChatGPT as I would talk to a junior engineer. I had all the rules that are global in the context / personality section in the options and then I litterally exchange with him as a human junior engineer (which has the knowledge of a senior).

I realized lately that putting stict criterias can lead to hallucination so I added rules like:

  • Never guess
  • Never estimate
  • Always asks if you have a blocking point or a question

Generally speaking, I handle the strategy and taking the decisions whenever it is required and he handle the reflexion.

And for the codex prompt that are completely different, I actually use ChatGPT himself as the architect of the project, we discuss strategy and then he produce the prompts. I copy paste the prompts and then copy paste the answer. I do review the prompt to remove the rare hallucinations but it mainly help me understand which points were unclear for GPT.

Just so you know he is still extremely bad at anything that is linked to visual. He has very poor awareness of the positions of the objects compare to the other objects in a picture and or a render I want so I am adding a huge amount of factual details whenever I work on UX.

13

Sure I’d go something like this

"Make an announcement for LinkedIn about our new product.

Rules:

-Use short sentences.

-Use accessible English without C2 lexics.

-Use a CTA + our link in the end.

Dont’s: Don’t build many lists as you often love to, and don’t be too excited in style.

<< The product is … It helps to … and …>>"

15

Thank you all for the replies. This has been very helpful for me.

I don’t have many people around me who talk about prompting in this way.
I’m not an engineer, and most prompt examples I usually see are template-like, so I sometimes wondered if my way of working with ChatGPT was a bit unusual.

Reading your examples was genuinely encouraging.
It’s fun to see that other people are using similar patterns in practice.

I’m starting to think the useful distinction is not simply “natural language vs structured prompts,” but “which format works best at which stage of the workflow.”

For small tasks, natural language is often enough.
For larger tasks with many constraints, a structured format helps.

But in my experience, the strongest pattern is often:

natural conversation → structured artifact → execution or implementation

So maybe structured prompts are not always the starting point.
Sometimes they are an intermediate artifact created from the conversation.

16

Is important to be aware that the agents performance matters beyond the prompt. A friendly-repository to the agents is something to keep in mind as well.

AGENTS.md distributed in the root of the workspace and through the subfolders, useful SKILLS (either generics or adapted to your workflow), customized agents, hooks, rules, reusable workflows, etc. It is a lot of things and all of them is well explained in Codex Docs. As long as your codebase increase in size, is important to evaluate and adapt your agents guidance in the project.

Each of these agents guidance has a why, so don’t just drop random guidance through your repo or just copy/paste from another project, evaluate it before.

17

Thank you, that was very helpful.

To be honest, I had felt that files like AGENTS.md were probably not something I should touch casually, but I didn’t really know what they were.

After reading your explanation, I asked GPT several questions about it, and I’m now starting to understand persistent project guidance as something like a constitution or house rules for the repository.

That helped me understand the difference between a one-time handoff note and guidance that should remain in the project. Thank you.

18

One thing I noticed is that the word “guidance” itself may be confusing for non-engineers or non-native English speakers.

At first, I imagined it as something closer to a system configuration file or a script that should not be changed casually, rather than a human-readable set of project rules.

So the idea that people might design, adapt, or copy such guidance was new to me.

I had not even opened AGENTS.md before, because I did not expect it to contain human-readable text.

Now that I think about it, it is a .md file, so of course it makes sense that it is readable and editable as Markdown.

For me, most files in the repository do not feel like things I created myself from scratch.

They feel more like parts of a small product that Codex helped me build. So when I see an unfamiliar file, I tend to treat it like an internal component that I should not touch unless I understand it.

That may be one reason why AGENTS.md did not immediately look like a human-editable document to me.

19

Nice to see you so engaged and wanting to learn more about prompting.

I’m curious what use cases you mostly had in mind here. ChatGPT conversation, image generation, coding agents, writing tasks, reusable workflows or something else?

If you have a specific use case in mind, it may be easier for others to recommend relevant topics, docs or examples.

Right now the question is very broad, so narrowing it down a bit could help people give more practical answers instead of general prompting advice​:slightly_smiling_face:

20

Thanks, Larisa — that makes sense.

I’m not focusing on one specific domain right now. My original question was more general: when are long structured prompts better, and when is interactive iterative prompting better?

After reading the replies, my takeaway is that a hybrid approach seems best: interactive prompting for upstream exploration and clarification, and structured templates for downstream execution and consistency.

That answers my question pretty well, so I’ll mark the topic as solved. Thanks!