Why do users struggle with prompts? A simple solution before sending

Prompting
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1

Proposed by Shoval Zohar Turgeman – based on real-world user interaction analysis

Hi everyone,

I would like to share a product suggestion based on hands-on experience working closely with real users interacting with AI systems.

For those who prefer a quick overview:

Many users struggle to write effective prompts, even when they know what they want.

Idea: Before sending a prompt, let the system suggest several improved versions (e.g., concise, detailed, structured).

The user selects the version that best matches their intent → leading to better responses, less trial-and-error, and improved prompt-writing skills over time.

I’d really love to hear your thoughts on this idea — does this resonate with your experience?
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For those who want a deeper dive:

Through ongoing work with users, I consistently observe a recurring challenge: many users struggle to formulate effective prompts. Even when their intent is clear, the way requests are phrased often leads to suboptimal or misleading responses.

Problem

As a result:

  • Users need to repeat or rephrase queries multiple times
  • Response quality is inconsistent
  • Trust in the system may be reduced

While AI systems sometimes compensate by improving responses or implicitly refining user intent, there is currently no structured mechanism that actively guides users before submitting their prompt.

Existing Gap

Most current approaches focus on:

  • Rewriting or improving text after it is written
  • Generating better responses despite imperfect prompts

However, they do not fully address:

  • Understanding user intent before submission
  • Structuring prompts in alignment with the user’s actual objective

Proposed Solution: “Improve Prompt” Before Send

Introduce a capability that improves the prompt before it is sent.

Key components:

  1. Intent-aware prompt refinement
    The system analyzes the user’s input and restructures it into a clearer and more effective prompt, aligned with the user’s intent.

  2. Multiple prompt suggestions
    Instead of one version, the system suggests several variations:

  • A concise version
  • A detailed / analytical version
  • A structured / task-oriented version

The user selects the version that best matches their intent.

  1. User-controlled execution
    The selected prompt is inserted into the input field and sent by the user, ensuring transparency and control.

Additional Value

Even if the suggestion is not perfect, this feature still provides strong value:

  • Helps users learn how to think and write better prompts
  • Improves interaction patterns over time
  • Acts as an educational layer for prompt literacy

Expected Impact

  • Improved response quality without changing the model
  • Reduced trial-and-error
  • Increased user trust
  • Potential reduction in token usage
  • Better alignment between user intent and output

This proposal is based on my own analysis and experience working with real users.

I would appreciate acknowledgment if similar concepts are considered or implemented in the future.

Suggested Interaction Flow

Would a workflow like the following be useful in your experience?

Prompt Improvement Flow:

  • The user writes an initial instruction or question
  • The user clicks an “Improve Prompt” button
  • The system generates several improved prompt variations
  • Each variation is structured to better align with the user’s intent and improve response quality
  • The user reviews the suggested options
  • The user selects the prompt that best reflects their intention
  • The selected prompt is inserted as the input and sent

Beyond improving response quality, this flow may also help users develop a better understanding of how to formulate effective prompts over time.

Curious to hear:

  • Do others experience similar issues with users struggling to formulate prompts?
  • Would this kind of guided prompt selection add value to your workflows?
  • Would you personally use a feature like this?

Best regards,
Shoval Zohar Turgeman

2

This approach addresses a real friction point, especially for users who understand their goal but struggle with structuring it effectively. Offering multiple prompt variations could reduce repetition and improve output consistency. It also has potential as a learning tool, helping users gradually internalize better prompt construction without adding complexity to the workflow.

3

Prompt creation is a friction point for me. I created a prompt handling instruction set (see below) to help myself.

So far so good! Prompting has improved over the past 2-3 weeks using it. I also include shortcut commands to my sets to change settings based on my needs during chat sessions.

  • I do like the idea of having an “Improve Prompt” icon button.
    If anyone has suggestions on improving the instruction set, I’m all ears!
Prompt Handling Instruction Set 
##Prompt Handling Instructions

1. Review the user’s prompt.
2. Before answering, include the exact heading: **Recommended Improvement Prompt**
3. Under that heading, either:
- provide a clear, concise recommended version of the user’s prompt, or
- state: **Prompt is Efficient.**
4. Insert this divider on its own line: ---
5. Then answer the user’s request directly, matching the requested tone and purpose.
6. Do not include any part of the answer above the divider.

##Prompt Handling Commands

- `/recommend_prompt` on = include the recommended prompt
- `/recommend_prompt` off = skip the recommended prompt
- `/prompt-only` = generate only the recommended prompt
- `/answer-only` = generate only the answer
- `/compact` = keep the recommended prompt short
- `/full` = allow a more detailed recommended prompt
- `/reset` = restore Prompt Handling Instructions
4

That’s really interesting — I like the structured approach you built, especially the idea of controlling how prompts are handled within the flow.

What I had in mind is slightly different and more from a product/UX perspective:

Instead of generating a single improved prompt within the response, the idea is to present multiple prompt options before sending, each reflecting a different interpretation or level of detail.

For example:

  • A concise version
  • A more detailed version
  • A structured/task-oriented version

The user would then choose the option that best matches their actual intent.

The key idea is not just improving the prompt, but helping the user realize what they actually meant to ask.

In a way, this creates a small “pause moment” before the system responds — where the system tries to understand the intent, and the user actively confirms it.
This adds a sense of transparency, control, and mutual alignment between the user and the system, instead of the system just “guessing” and responding.

Your approach already shows there’s real friction here — which is exactly what made me think about this direction.

Curious — do you think having multiple options instead of a single recommendation would be useful in your setup?

5

Sounds to me that this is just an special implementation of ChatGPT. Or am I missing something?

6

That’s a fair question - I think the key difference is in where this happens in the interaction.

Today, tools like ChatGPT may implicitly improve or reinterpret prompts as part of generating a response.

What I’m suggesting is a step before the response is generated - a structured UX layer where the system explicitly offers multiple prompt interpretations, and the user chooses the one that best reflects their intent.

So it’s less about how the model answers, and more about improving the alignment before the model answers.

Also, even if some of these capabilities exist in different forms - we still see that many users struggle to write effective prompts.

Which raises an important point:
If this problem still exists at scale, it likely means the solution isn’t accessible or intuitive enough for most users.

The goal here is to make prompt guidance a built-in, natural part of the experience - not something users need to learn, configure, or explicitly ask for.

To my knowledge, that explicit “pre-send prompt selection” flow doesn’t currently exist as a built-in interaction pattern.

7

I can understand that this would be beneficial for simple use cases, but for more complex cases that require specific domain knowledge, users would not really need this. Do you agree? My customers do a lot of document analysis - I have yet to hear of them having prompting issues. Furthermore, the reasoning models have become so powerful, that responses are more nuanced than ever.

8

That’s a really interesting point - and I agree that in more structured or mature workflows (like document analysis with experienced users), the friction may be less visible.
However, I see it a bit differently:
Even for advanced users, it’s not always about “fixing bad prompts”, but rather about refining intent.

Small changes in phrasing can still have a significant impact on:

  • the scope of the answer
  • the level of detail
  • what the model chooses to focus on

So even if users don’t explicitly report “prompting issues”, there could still be hidden inefficiencies or missed precision.
From my own experience working closely with users, I often see that many of them struggle to clearly formulate what they want to ask.
When I guide them and show how even a small change in the prompt can improve the outcome, they quickly realize how critical this step is.
That’s actually what led me to think about this idea - as a way to reduce that gap upfront, instead of relying on users to learn it through trial and error.
Also, many experienced users tend to develop their own internal patterns or habits - which works, but requires learning and adaptation.
The idea here is to make this layer explicit and accessible:
not just for beginners, but as a structured way to explore different interpretations of the same request.
So instead of assuming one “best prompt”, the system helps surface multiple valid directions - and allows the user to choose.
At the end of the day, I agree it also depends on the use case and the goal of the system.

I’m curious - do you think your users would benefit from seeing alternative prompt interpretations, even if they already have working patterns?

And really - thanks for sharing your perspective.

9

As with a project like this, “The devil is in the details.” A working prototype would answer that question. Otherwise, this discussion is merely academic.

10

From a user perspective, it often looks like a prompting issue—people struggle to express what they want, and small changes in wording can significantly affect the result.

But structurally, there’s also a system-level constraint that might explain why this keeps happening.

Most current LLM systems (including GPT-style models) operate in a single input → single response pattern.

That means a potentially multi-dimensional intent gets compressed into a single prompt, and the model effectively selects one interpretation in a single pass.

So when results feel “off,” it’s not always because the prompt is bad—it can also be because:

  • multiple valid interpretations exist
  • only one is explored
  • no comparison or selection happens

A simple example:

“Explain this document”

This could reasonably mean:

  • a concise summary
  • a structured breakdown
  • a critical analysis

All are valid, but they lead to very different outputs.

Right now, we rely on the model to implicitly choose one.
If it chooses a different direction than intended, it appears as a prompting problem.

One possible direction is to move toward:

single request → multiple interpretations → selection

Either exposed in the UX, or handled internally.

That said, this probably doesn’t apply equally to all tasks—especially in workflows where the interpretation space is already constrained (like certain document analysis pipelines).

But in cases where a request can be understood in multiple valid ways, this might help reduce hidden inefficiencies that aren’t always obvious, even to experienced users.

At that point, the focus shifts slightly—from writing the “perfect prompt” to how the system explores and selects among possible interpretations.

Curious how this would fit into the kinds of workflows you’re seeing.

One thing I’ve been thinking about as well is that this kind of approach introduces trade-offs.

Exploring multiple interpretations can improve alignment with user intent, but it also raises questions around:

  • how much choice to expose without overwhelming users
  • how to balance quality vs latency/cost
  • when to rely on system selection vs user selection

So it’s probably not a universal solution, but more of a design space with different trade-offs depending on the use case.

11

This is a really insightful breakdown -I especially like how you framed it as a structural limitation rather than just a prompting issue.

I agree that what often looks like a “prompting problem” is actually a result of compressing multi-dimensional intent into a single input → single output interaction.

That’s exactly the gap I was trying to point at -but from a more UX-oriented angle.

The idea isn’t to apply this everywhere, but to introduce a lightweight “interpretation layer” before execution -specifically in cases where ambiguity is likely.

For example, instead of directly answering a query like “Explain this document”, the system could briefly surface 2–3 high-confidence interpretations (e.g., summary, structured breakdown, critical analysis), and let either the user choose or the system auto-select based on context.

The value here is twofold:

  1. It reduces misalignment early -before generating a full response

  2. It turns implicit assumptions into explicit choices, which improves both accuracy and user trust

This doesn’t have to add significant friction -it could be triggered selectively, for example:

  • when the model detects multiple high-probability intents

  • when similar prompts historically led to divergent outputs

  • or when user queries are short / underspecified

So rather than solving prompting in general, it’s about catching “high-ambiguity moments” and handling them differently.

I fully agree this introduces trade-offs (UX complexity, latency, cost), but in ambiguous, open-ended tasks, I think the gain in alignment could outweigh them.

That said, it might slightly interrupt the natural flow by introducing a small pause and a suggestion layer -but ultimately, it remains fully in the user’s control. If they choose to engage with it, it likely only improves the outcome rather than adding friction.

In addition, in many cases, this could also reduce the number of retries or prompt iterations, which is where a lot of hidden inefficiency exists today.

Curious how you’d think about implementing something like this -especially the threshold for triggering it without disrupting the flow.

12

I’m curious to see how deep you guys are going to take this :eyes:

13

I agree that this shouldn’t be a default behavior for every request. Framing it as a lightweight “interpretation layer” that activates under ambiguity makes a lot of sense from a UX perspective.

Instead of thinking in terms of a fixed rule, it might be more useful to treat activation as a combination of signals, such as:

  • Ambiguity in the request (short, underspecified, or open-ended prompts)
  • Divergence risk (cases where different interpretations would lead to very different outputs)
  • Model uncertainty (e.g., low confidence or high variance across possible completions)
  • History inconsistency (similar inputs previously leading to different outcomes)

When one or more of these cross a threshold, the system could briefly surface 2–3 likely interpretations — either explicitly (UX) or implicitly (internal selection).

The key, as you pointed out, is to keep this lightweight:

  • default: single-pass response
  • conditional: interpretation layer activates
  • optional: user can explore or override

So it’s less about adding friction everywhere, and more about inserting a small “pause + align” step only when the risk of misalignment is high.

One extension that might be interesting is to separate what happens internally vs what gets exposed to the user.

For example, the system could generate multiple interpretations internally by default (to reduce misalignment risk), but only surface them when ambiguity crosses a threshold.

This creates a layered approach:

always-on internal exploration + conditional external exposure

That way, the UX stays simple in most cases, while still retaining the benefits of multi-interpretation when it matters.

I also like your point about reducing hidden inefficiencies — in many cases, this could replace multiple prompt iterations with a single, better-aligned interaction.

More broadly, this feels like part of a small design space rather than a single solution. For example:

  • Multi-interpretation + selection
  • Intent locking (clarify first, then execute)
  • Iterative refinement (converge through feedback)

These approaches aren’t mutually exclusive and could be combined depending on the workflow and level of ambiguity.

Curious how you’d think about prioritizing these signals — would you lean more on model-side uncertainty, or user/input-side heuristics?

14

This is a really thoughtful extension -I like how you framed activation as a combination of signals rather than a fixed rule.

The internal vs. external separation is especially compelling.
Always-on internal exploration with conditional exposure feels like a strong way to reduce misalignment without constantly interrupting the user flow.

In many cases, this could also reduce repeated prompt iterations, which is where a lot of hidden inefficiency and user frustration exist today.

On your question -I’d lean toward starting with user/input-side heuristics as the primary trigger.
They’re simpler, more transparent, and can be detected upfront (e.g., short or underspecified prompts).

Model-side signals (like uncertainty or divergence) can then act as a second layer -validating whether that ambiguity actually leads to meaningfully different outcomes.

So in practice, I’d think about it as:
input signal → model validation → optional exposure

The value here is catching ambiguity early, but only surfacing it when it actually matters -which helps reduce unnecessary friction while still improving alignment.

More broadly, I see this less as a single solution and more as a layer that can complement other approaches (like clarification-first or iterative refinement), depending on the workflow.

15

It feels like this has naturally evolved into a much clearer structure.

What started as a UX idea is now well-defined as a selective layer that activates under ambiguity, using input-side heuristics and model-side validation to decide when to intervene.

Framing it this way makes the role much clearer — not as a replacement for the default flow, but as a complementary layer that helps manage misalignment when needed.

16

It would be interesting to see how something like this could be explored in real-world systems - especially in platforms like OpenAI, where improving alignment at the UX layer could have a meaningful impact at scale.