Prompt Symptoms vs Model Symptoms

How to Tell What’s Actually Broken (and Fix It Fast)

One of the biggest mistakes AI designers make is fixing the wrong thing.

They tweak prompts when the model is wrong.
They switch models when the prompt is weak.

This guide teaches you how to diagnose the failure correctly.

The Core Principle

Prompts control behaviour.
Models control capability.

If the model can’t do the job, no prompt will save it.
If the model can do the job, a bad prompt will still fail.

Your job is to know which one you’re dealing with.

High-Level Difference

Prompt Problems

• The model understands the task

• Output is close, but flawed

• Improvements are incremental

• Fixable with clearer language

Model Problems

• The model fights you

• Output breaks fundamental requirements

• Results feel random or unstable

• No amount of prompting fixes it

Prompt Symptoms (Fix the Prompt)

These mean the model is capable, but your instructions aren’t clear enough.

Prompt Symptom 1: The Output Is Close, But Slightly Off

Looks like:

• Composition mostly right

• Style mostly right

• Small inaccuracies

Diagnosis: Prompt issue

Why:
The model understands the task but lacks specificity.

Fix:

• Add clearer descriptors

• Remove vague words

• Define materials, lighting, camera, mood explicitly

Prompt Symptom 2: Inconsistent Style Across Generations (Same Model)

Looks like:

• Same model

• Same general idea

• Style drifts slightly each time

Diagnosis: Prompt issue

Why:
You haven’t locked the style language tightly enough.

Fix:

• Standardise style descriptors

• Reuse phrasing

• Introduce reference images or structured prompts

Prompt Symptom 3: The Model Ignores Secondary Details

Looks like:

• Main subject correct

• Small elements missing or wrong

• Accessories, props, background details inconsistent

Diagnosis: Prompt issue

Why:
Priority weighting isn’t clear.

Fix:

• Reorder prompt by importance

• Reduce clutter

• Make secondary elements explicit but concise

Prompt Symptom 4: Results Improve Gradually With Tweaks

Looks like:

• Each iteration is better

• You feel progress

• Small changes matter

Diagnosis: Prompt issue

Why:
This is how prompt optimisation should feel.

Fix:
Keep refining. You’re on the right track.

Model Symptoms (Switch the Model)

These mean the model is the wrong tool, regardless of prompt quality.

Model Symptom 1: You Keep Adding Constraints, Results Get Worse

Looks like:

• Longer prompts

• More negatives

• Less control

• More chaos

Diagnosis: Model issue

Why:
The model is optimised for freedom, not precision.

Fix:
Switch to a control- or realism-focused model.

Model Symptom 2: You Can’t Repeat a Good Result

Looks like:

• One great output

• Everything else misses

• Same prompt ≠ same result

Diagnosis: Model issue

Why:
The model is low-consistency by design.

Fix:
Move to a model or workflow designed for repeatability, or stack.

Model Symptom 3: Text Is Always Broken (No Matter What)

Looks like:

• Warped letters

• Soft typography

• Logos melting

Diagnosis: Model issue

Why:
The model treats text as visual noise, not information.

Fix:
Switch to an editing- or text-preserving model.

Model Symptom 4: Faces, Products, or Objects Drift Every Time

Looks like:

• Identity changes

• Shape inconsistencies

• Material drift

Diagnosis: Model issue

Why:
The model is optimised for variation, not identity.

Fix:
Use a realism-focused model, references, or stacking.

Model Symptom 5: Small Edits Break Everything

Looks like:

• Ask for one change

• Entire image regenerates

• Style collapses

Diagnosis: Model issue

Why:
The model can’t do controlled edits.

Fix:
Switch to an editing-first model instead of regenerating.

The Fast Diagnostic Table

The 30-Second Diagnostic Test

Ask yourself:

1. Does the model understand what I want?

2. Is it capable of this type of output?

3. Are failures structural or cosmetic?

4. Am I refining or wrestling?

If it feels like wrestling → change the model
If it feels like refining → improve the prompt

The Professional Mindset Shift

Beginners ask:

“How do I prompt better?”

Professionals ask:

“Is this the right tool for this job?”

Prompting skill matters.
Model selection matters more.

Outcome for Art Input Members

After using this guide, members should:

• Stop over-engineering prompts

• Switch models sooner

• Waste fewer credits

• Build calmer workflows

• Deliver more consistent results

Final Rule

If the failure is structural, switch the model.
If the failure is cosmetic, fix the prompt.

Everything else is noise.