Why Structure Your Prompts

There are two types of prompting styles mainly: A thought dump where you blurt out whatever it is that you want, and a more refined structured prompt where you almost codify what you want.

I was reading a bit of chatter online about LLMs responding better (will explain what better means here a bit later in the essay) to structured prompts, specifically formatted as XML or JSON. Personally I’ve always been a lazy LLM user who mostly relies on voice and occasionally types out poorly structured prompts so I really wanted to understand what writing structured prompts was all about.

Intuitively I knew that writing natural language prompts basically offered no guardrails on the output. I mean the output to the same prompt would vary anywhere between a little bit to a lot when you fire it again. This is not a problem for the most day to day tasks but it definitely starts to matter the moment you indulge in doing serious work with LLMs.

Whether you’re a designer refining copy, a product manager testing flows, a support rep scaling tone and customer-facing communications, or a marketer tuning conversion messages, structured prompting ends up becoming your only way to make LLMs work reliably and predictably for you.

After a lot of digging around and reading online, there are a few very clear advantages I found that structured prompting offers.

Let’s dive in:

Step-by-step task chaining

The first thing I noticed: structured prompts helped preserve state across turns.

In natural language, you might say “Make the image warmer,” then “Add some grain,” then “Remove the highlights.” Each new instruction depends on the model correctly recalling and respecting the earlier ones.

But with structure, you’re showing the entire state up front:

<edit>
  <temperature>+20</temperature>  <!-- add warmth -->
  <grain>medium</grain>
  <highlights>30</highlights>
</edit>

Want to change the temperature?

<edit>
  <temperature>+10</temperature>  <!-- reduce warmth -->
  <grain>medium</grain>
  <highlights>30</highlights>
</edit>

Or remove the highlights?

<edit>
  <temperature>+10</temperature>
  <grain>medium</grain>
  <highlights>0</highlights>  <!-- remove highlights -->
</edit>

I start realizing this is more predictable. I’m not re-explaining the intent; I’m just modifying the configuration point by point.

Even with temperature set to zero (meaning no randomness), the structured version feels more stable across runs. With natural prompts, the path is fuzzier. Saying “Remove the highlights” might accidentally tweak other things the model inferred earlier, like contrast or shadows. The structured version gives me clearer control over individual steps.

Tweaking precisely without errors

One of the most powerful aspects of structured prompts is their ability to let you make precise changes while keeping everything else exactly as is. Think of it like a mixing board in a recording studio - you can adjust just one slider without touching the others.

I experiment with this by running several rounds of edits on an image, focusing on temperature adjustments (+20, +10, 0) while wanting other parameters to stay stable.

With natural language, I find myself having to be increasingly verbose and careful:

“Make it slightly less warm, but don’t change anything else I set before.”

“Now make it completely neutral temperature, but keep the grain and everything else exactly the same.”

It works, but you’re constantly having to remind the model what should stay unchanged. There’s always a risk that the model might misinterpret or forget previous settings.

With structured prompts, that mental overhead disappears completely. The structure itself preserves the state:

<edit>
  <temperature>+20</temperature>  <!-- adjust just this -->
  <grain>medium</grain>          <!-- these stay -->
  <highlights>30</highlights>    <!-- exactly as is -->
</edit>

Whatever I didn’t explicitly modified stayed locked in place. No need to say “keep everything else the same” - the structure enforced that automatically. This made iterative tweaking much more reliable and significantly faster.

It’s like having version control for prompts - you can see the exact state, make a precise change, and trust that nothing else will drift. This precision becomes invaluable when you’re trying to perfect something through multiple iterations.

Undoing without ambiguity

I got curious about how each approach handles undoing changes. Let’s compare how natural language and structured prompts handle a two-step process: first applying a change, then trying to undo it.

And here is where the value of structure became clearest to me. The structured prompt offered a clean, deterministic way to roll back changes. I could target just the attribute I wanted to undo while preserving everything else. The model was no longer left guessing what I meant by “undo.” It was told exactly what the state should be. That precision is hard to match with prose.

It made experimentation safer. Iteration faster. And it meant I could go forward and backward in time with edits, without uncertainty creeping in.

Tuning tone with clarity

Let’s say you’re writing a message to a customer about a refund being denied. In natural language, you might write:

“Tell the customer we can’t issue a refund, but say it nicely.”

That works fine if you’re doing it once or twice. But what if you’re handling hundreds or thousands of customer messages a day? You’d want some consistency, in tone, in clarity, in overall style. That’s where structure starts to shine.

Here’s an example of that same message turned into a structured prompt:

{
  "task": "compose_customer_response",
  "intent": "refund_denial",
  "tone": "polite_but_firm",
  "constraints": {
    "length": "short",
    "no_blame_language": true
  }
}

Now the tone becomes just one of the dials you can tweak. Try empathetic, neutral_professional, or informal_friendly, and you’ll get three distinct responses, but each one still aligns to the same core instruction. It’s like designing different outfits with the same measurements.

When you need repeatability across teams or systems, turning voice and intent into parameters is what helps you scale without losing control. For someone unfamiliar with JSON, think of it as a settings panel, where each value controls how the message comes out.

Prompts as programmable components

Eventually it starts to feel less like prompting and more like system design.

• Keys are props
• Values are config
• The prompt itself feels like a component

That means I can debug. I can diff versions. I can isolate changes to just one thing. It reminds me of working with design systems, not copywriting.

Structure makes prompts system-friendly

Things get more interesting when I try plugging structured prompts into actual product flows. Suddenly it isn’t just about making a better response, the structure lets me do more:

• I can validate prompt fields before sending them.
• I can generate them from templates.
• I can connect them to data, CMS metadata, customer type, app state.

One small experiment is a support prompt router. One field controls tone, one the task, another lists constraints. Each field scopes the generation in a clear way. It’s a simple prototype, but it shows me how structure helps prompts plug into other systems more smoothly.

I realize I’m also reducing prompt injection risk without even trying. User input is confined to a specific field, separated from the rest of the prompt logic.

Repeatability and production-grade control

One of the most valuable things structure unlocks is repeatability. When you are running prompts across thousands of records, or using them as part of workflows in production systems, you need consistency. You cannot afford subtle shifts in tone, instruction drift, or unexpected formatting.

Structured prompts give me confidence that the model is reading and responding to a clear spec. The prompts are no longer ephemeral bits of chat history — they become reusable configurations.

That means I can run the same instruction a thousand times, tweak just one parameter, and know the output will stay in bounds. This is exactly what agents need to work safely in production. Without that determinism, prompts feel like fragile instructions that might break at scale.

Let’s see this in action with some practical examples.

Experiments to try

Let’s look at three real-world scenarios where structured prompts make a difference. For each example, we’ll compare natural language versus structured approaches, and examine how consistent the outputs remain when running the same prompt multiple times.

1. Tone testing for customer support

Customer support requires consistent messaging, especially for sensitive topics like refund denials. Here’s what happens when we ask the model to compose a refund denial message - first using natural language, then using a structured format. Pay attention to how the tone and messaging varies when we run the same prompt twice:

Try changing just the tone. See how consistent the rest of the message stays.

2. Image editing instructions

Photo editing often requires precise, repeatable adjustments. When we want to make specific changes like adjusting warmth or removing glare, clarity in instructions becomes crucial. Let’s compare how natural language versus structured commands handle these editing instructions:

Then change just the temperature value. Does the model keep the glare setting untouched?

My good friend Rahul has been experimenting with editing images and inventing new styles with JSON prompts for a while now.

Check out all the styles he’s come up with here. They’re pretty cool.

The interesting difference in his approach with defining style and how Midjourney did the same with –sref tags is that the –sref tags obscured away all the granular details of the style. JSON styles allow users to edit, remix and invent their own styles, which is a much more open and collaborative approach.

For remixing styles and controlling the output to a few variables, the structured prompting approach works quite well.

3. Short copy for a CTA button

Writing call-to-action (CTA) button text requires a delicate balance - it needs to be concise, compelling, and targeted to the right audience. Here’s how the model handles this task with both approaches, particularly when we need consistent messaging across our app:

Try these once with prose and once with structure. The difference won’t always be dramatic — but you’ll notice where structure creates stability, and where prose offers flexibility.

While the examples above demonstrate the power of structured prompting, it’s important to understand when to use it. Structured prompting does not come for free. There’s a real overhead in defining the structure itself, choosing which fields matter, setting defaults, and enforcing consistency. It is not ideal for every use case.

If you’re doing one-off queries, creative exploration, or rapid ideation, prose prompts are usually faster and more fluid. They’re easier to write and iterate on casually.

But if you’re building something repeatable, something shared with others, or something that needs to run across hundreds of cases, structured prompts start to show their value. They let you scale tone, format, and constraints predictably. They plug more easily into templates, interfaces, and workflows. And they help the model understand exactly what you want to control, and what you want to stay the same.