I've been generating AI images since the early Midjourney days, and I'll be honest with you — most of the time it felt like playing slot machines with prompts. You'd write something, hope for the best, and maybe 1 out of 10 generations would actually match what you had in your head.
That changed yesterday.
OpenAI dropped GPT-image-1.5 on December 16, 2025, and along with it, they published something I've never seen from them before: an actual, practical, step-by-step prompting guide that tells you exactly how to get the results you want. Not vague suggestions. Not marketing fluff. Real production-ready techniques.
I spent the last 24 hours going through every example in their official cookbook, testing the prompts, and pulling out everything you actually need to know. This article is my attempt to condense that 30+ minute read into something you can scan in 10 minutes and start using immediately.
Let's dive in.
What Makes GPT-image-1.5 Different (The Honest Assessment)
Before we get into the prompting techniques, let me give you the straight facts about what this model actually brings to the table.
The headline improvements according to OpenAI include up to 4x faster generation speeds compared to GPT-image-1 (their April 2025 release), significantly better text rendering inside images, stronger identity preservation when editing photos, and 20% lower API pricing for developers.
From my testing, the speed claims hold up. What used to take 8-12 seconds now comes back in 2-4 seconds on the "low" quality setting. The text rendering is genuinely impressive — you can now generate infographics with readable small text, which was essentially impossible before.
But let's be real about the limitations OpenAI themselves acknowledge: the model still has issues with certain drawing styles, makes mistakes on images requiring scientific knowledge, and can occasionally drift when you're doing multi-step editing sessions. This isn't magic — it's a tool that requires understanding to use well.
That's exactly why the prompting guide matters so much.
The Prompting Fundamentals That Actually Work
OpenAI's guide opens with eight core principles. These aren't suggestions — treat them as rules if you want consistent results.
Structure your prompts in a specific order. The model performs best when you follow this sequence: background/scene first, then subject, then key details, then constraints. Also include your intended use case (like "ad," "UI mock," or "infographic") because this sets what OpenAI calls the "mode" and determines the level of polish the model aims for.
For complex requests, break things into short labeled segments or use line breaks instead of cramming everything into one massive paragraph. The model parses structured prompts better than stream-of-consciousness descriptions.
Be concrete about everything visual. Don't say "nice texture" — specify materials, shapes, and the visual medium you want (photo, watercolor, 3D render). When you need photorealism specifically, camera and composition terms work better than generic quality markers.
Here's a counterintuitive finding from the guide: saying "8K ultra-detailed masterpiece" doesn't actually help much. What works is specificity about lens, aperture, and lighting. Terms like "50mm lens," "shallow depth of field," and "soft coastal daylight" steer realism far more effectively than superlatives.
Understand the speed versus quality tradeoff. The model offers quality settings — "low" and "high." For anything involving dense layouts or lots of in-image text, use "high." For rapid iteration or high-volume use cases, start with "low" and test whether it meets your needs. Many use cases don't require maximum fidelity, and the speed difference is substantial.
Control composition explicitly. If the layout matters to you, you need to specify it. Call out framing (close-up, wide, top-down), perspective (eye-level, low-angle), lighting conditions (soft diffuse, golden hour, high-contrast), and placement ("logo top-right," "subject centered with negative space on left").
State your constraints clearly. This is where most people fail. You need to tell the model what NOT to do. Add exclusions like "no watermark," "no extra text," "no logos/trademarks." For editing tasks, use "change only X" and "keep everything else the same." Then repeat those preserve instructions on each iteration because the model can drift otherwise.
Handle text in images deliberately. Put literal text in quotes or ALL CAPS. Specify typography details including font style, size, color, and placement. For tricky words like brand names or unusual spellings, spell them out letter by letter. The guide notes this improves character accuracy significantly.
Reference multiple images correctly. When using multiple input images, reference each by index and description: "Image 1: product photo… Image 2: style reference…" Then describe explicitly how they should interact: "apply Image 2's style to Image 1." For compositing, specify exactly which elements move where.
Iterate instead of overloading. Start with a clean base prompt, then refine with small single-change follow-ups. Use references like "same style as before" or "the subject" to leverage context. But re-specify critical details if they start to drift — the model doesn't have perfect memory across generations.
Text-to-Image Generation: The Complete Workflow Breakdown
Let me walk you through each major use case OpenAI covers, with the exact prompt patterns that work.
Creating Infographics That People Can Actually Read
This is where GPT-image-1.5 genuinely shines compared to previous models. You can now generate technical diagrams, explainers, labeled visuals, and data visualizations with legible text.
The key insight from the guide: for infographics with dense information, always set quality to "high." The low setting produces faster results but struggles with text clarity at small sizes.
Here's the prompt structure that works:
Create a detailed infographic [showing/explaining] [topic].
Include [specific elements you need: flow diagrams, labeled components, statistics].
Target audience: [students/executives/general public].
Visual style: [clean/technical/friendly].
For a practical example, OpenAI's guide uses this prompt to explain how an automatic coffee machine works:
"Create a detailed Infographic of the functioning and flow of an automatic coffee machine like a Jura. From bean basket, to grinding, to scale, water tank, boiler, etc. I'd like to understand technically and visually the flow."
The result is a clear diagram showing the complete flow of coffee production with labeled components. What's remarkable is that the text labels are crisp and readable — something that would have required post-processing in earlier models.
One thing worth noting: the model has built-in world knowledge. You don't need to explain what a Jura machine is or describe every component. The model understands common objects, mechanisms, and concepts.
Translating Text Within Images
This use case surprised me. You can take an existing infographic, diagram, or designed asset and translate all the text to another language while preserving the exact layout, typography hierarchy, and design elements.
The prompt pattern is straightforward:
Translate the text in the [infographic/design] to [target language].
Do not change any other aspect of the image.
This is huge for localization workflows. Instead of rebuilding assets from scratch for different markets, you can generate localized versions in seconds. The model preserves spacing, font sizes, and placement while swapping the language.
Testing this myself, it handles major languages well. Romance languages, German, Chinese, Japanese, and Korean all produced readable results. The spacing adjustments for languages with different character widths were handled automatically.
Photorealistic Images That Don't Look AI-Generated
Getting genuine photorealism requires a different approach than other styles. The guide emphasizes prompting "as if a real photo is being captured in the moment."
The prompt structure for photorealism:
Create a photorealistic [candid/documentary/portrait] photograph of [subject with specific physical details].
Shot like a [35mm film/digital/medium format] photograph, [framing] at [angle], using a [lens focal length].
[Lighting conditions], [depth of field], [color treatment].
The image should feel [honest/unposed/natural], with [specific texture details].
No [glamorization/heavy retouching/stylization].
Here's the example prompt from the guide that produces remarkable results:
"Create a photorealistic candid photograph of an elderly sailor standing on a small fishing boat. He has weathered skin with visible wrinkles, pores, and sun texture, and a few faded traditional sailor tattoos on his arms. He is calmly adjusting a net while his dog sits nearby on the deck. Shot like a 35mm film photograph, medium close-up at eye level, using a 50mm lens. Soft coastal daylight, shallow depth of field, subtle film grain, natural color balance. The image should feel honest and unposed, with real skin texture, worn materials, and everyday detail. No glamorization, no heavy retouching."
The key elements making this work: specific physical imperfections (wrinkles, pores, sun texture), photography terminology (35mm, 50mm lens, shallow depth of field), explicit anti-stylization instructions (no glamorization, no heavy retouching).
For the high-quality setting, you add quality="high" to your API call. This is recommended for any photorealism work where you need maximum detail.
Leveraging World Knowledge
This capability wasn't available in earlier image models and it's genuinely useful. GPT-image-1.5 has reasoning capabilities and strong world knowledge built in.
The example OpenAI gives is asking for a scene set in "Bethel, New York on August 16, 1969." The model correctly infers this is Woodstock and generates an appropriate period-accurate crowd scene without being explicitly told about the festival.
This means you can reference:
- Historical events by time and place
- Cultural moments indirectly
- Technical concepts without full explanations
- Geographic locations with appropriate contextual details
The practical application: you don't need to over-explain everything in your prompts. The model can fill in contextually appropriate details when you give it enough situational information.
Logo Generation That's Actually Usable
Logo generation has always been weak for AI models. The outputs tend to be either too complex (unreadable at small sizes) or too generic (clipart-feeling).
The guide offers a prompt structure specifically designed for production-ready logos:
Create an original, non-infringing logo for [company name], a [business description].
The logo should feel [personality adjectives: warm, modern, professional, playful].
Use clean, vector-like shapes, a strong silhouette, and balanced negative space.
Favor simplicity over detail so it reads clearly at small and large sizes.
Flat design, minimal strokes, no gradients unless essential.
Plain background. Deliver a single centered logo with generous padding.
No watermark.
You can also generate multiple variations in a single call by setting n=4 (or up to your preferred number). This gives you a batch of concepts to evaluate.
From the guide's example for a bakery called "Field & Flour":
"Create an original, non-infringing logo for a company called Field & Flour, a local bakery. The logo should feel warm, simple, and timeless. Use clean, vector-like shapes, a strong silhouette, and balanced negative space. Favor simplicity over detail so it reads clearly at small and large sizes. Flat design, minimal strokes, no gradients unless essential. Plain background. Deliver a single centered logo with generous padding. No watermark."
The constraint about "non-infringing" and "original" is important — it steers the model away from creating something that looks like existing brand marks.
Story-to-Comic Strip Generation
For visual storytelling, the guide shows how to generate multi-panel comic strips from narrative descriptions.
The approach: describe each panel as a clear visual beat. Keep descriptions concrete and action-focused rather than abstract.
Create a [orientation: vertical/horizontal] comic-style [format] with [number] equal-sized panels.
Panel 1: [Scene description with specific action and framing]
Panel 2: [Scene description with emotional beat or change]
Panel 3: [Scene description continuing narrative]
Panel 4: [Scene description with conclusion/payoff]
The guide's example tells a story about a pet while its owner is away:
"Create a short vertical comic-style reel with 4 equal-sized panels. Panel 1: The owner leaves through the front door. The pet is framed in the window behind them, small against the glass, eyes wide, paws pressed high, the house suddenly quiet. Panel 2: The door clicks shut. Silence breaks. The pet slowly turns toward the empty house, posture shifting, eyes sharp with possibility. Panel 3: The house transformed. The pet sprawls across the couch like it owns the place, crumbs nearby, sunlight cutting across the room like a spotlight. Panel 4: The door opens. The pet is seated perfectly by the entrance, alert and composed, as if nothing happened."
Note how each panel has specific visual details (paws pressed to glass, posture shifting, sunlight like a spotlight) rather than abstract emotional descriptions. This concrete approach produces cleaner panel compositions.
UI Mockups That Look Like Shipped Products
For interface design, the guide emphasizes describing the product "as if it already exists." Focus on layout, hierarchy, spacing, and real interface elements — avoid concept art language.
Create a realistic [device type] UI mockup for [product description].
Show [specific screens/sections with real content examples].
Design it to be [practical/usable/real-feeling adjectives].
[Color scheme], clear typography, and [style notes].
It should look like a real, well-designed app [context of use].
Place the UI mockup in [device frame].
OpenAI's example for a farmers market app:
"Create a realistic mobile app UI mockup for a local farmers market. Show today's market with a simple header, a short list of vendors with small photos and categories, a small 'Today's specials' section, and basic information for location and hours. Design it to be practical, and easy to use. White background, subtle natural accent colors, clear typography, and minimal decoration. It should look like a real, well-designed, beautiful app for a small local market. Place the UI mockup in an iPhone frame."
The key phrase here is "it should look like a real, well-designed app" — this anchors the model away from concept-y, artistic interpretations toward functional UI patterns.
Image Editing: The Edit Workflows That Transform Your Photos
This is where GPT-image-1.5 genuinely advances over previous generations. The editing capabilities allow you to modify existing images while preserving elements you want to keep.
The API call structure changes for editing — you're now using client.images.edit() instead of client.images.generate(), and you provide one or more input images.
Style Transfer That Actually Preserves Your Content
Style transfer lets you take the visual language (palette, texture, brushwork, grain) from one image and apply it to different content.
The prompt structure:
Use the same style from the input image and generate [new subject/scene] on [background specification].
The guide's example takes a pixel art style reference and applies it to a completely different subject:
"Use the same style from the input image and generate a man riding a motorcycle on a white background."
What makes this work: clearly separating what should stay consistent (style cues) from what should change (new content), and adding constraints about background to prevent unwanted additions.
For more control, you can specify which style elements matter most: "Match the color palette, brush texture, and level of detail from the reference. Generate [new subject]."
Virtual Try-On for E-commerce
This is a game-changer for product photography. You can dress a model in different clothing items while preserving their exact appearance.
The key insight from the guide: you need to explicitly lock everything about the person except their garments. Identity preservation requires specific instructions.
Edit the image to dress the [person] using the provided clothing images.
Do not change [their] face, facial features, skin tone, body shape, pose, or identity in any way.
Preserve [their] exact likeness, expression, hairstyle, and proportions.
Replace only the clothing, fitting the garments naturally to [their] existing pose and body geometry with realistic fabric behavior.
Match lighting, shadows, and color temperature to the original photo so the outfit integrates photorealistically, without looking pasted on.
Do not change the background, camera angle, framing, or image quality, and do not add accessories, text, logos, or watermarks.
You provide multiple images: the original person photo plus each clothing item you want to apply. The model composites them together while maintaining realistic draping and shadows.
From testing, this works best when your clothing images have clean backgrounds (ideally transparent or solid white). Busy backgrounds in the clothing photos can confuse the compositing.
Sketch-to-Photorealistic Rendering
This workflow transforms rough drawings into realistic images while preserving your original composition and proportions.
Turn this drawing into a photorealistic image.
Preserve the exact layout, proportions, and perspective.
Choose realistic materials and lighting consistent with the sketch intent.
Do not add new elements or text.
The constraint about not adding new elements is crucial — without it, the model may add trees, people, or objects that weren't in your original sketch.
For architectural sketches, interior designs, or product concepts, this workflow lets you visualize ideas without needing rendering software.
Product Extraction and Transparent Backgrounds
For catalog and marketplace images, you often need products isolated on transparent backgrounds with clean edges.
Extract the product from the input image.
Output: transparent background (RGBA PNG), crisp silhouette, no halos/fringing.
Preserve product geometry and label legibility exactly.
Optional: subtle, realistic contact shadow in the alpha (no hard cut line).
Do not restyle the product; only remove background and lightly polish.
The notes about "no halos/fringing" are important — these are common artifacts in background removal that make images look unprofessional. The model handles edge quality well when you specify these constraints.
Marketing Creatives with Readable Text
Creating ads and marketing materials with in-image text requires explicit typography constraints.
Create a realistic [format: billboard/poster/banner] mockup of [product/subject] in [scene setting].
[Format] text (EXACT, verbatim, no extra characters):
"[Your exact copy here]"
Typography: [font style], high contrast, [alignment], clean kerning.
Ensure text appears once and is perfectly legible.
No watermarks, no logos.
The guide emphasizes putting your exact copy in quotes and demanding "verbatim rendering." Without this, the model may add extra characters, change spelling, or duplicate text elements.
If text fidelity isn't perfect on the first try, the guide suggests keeping the prompt strict and iterating with small wording or layout adjustments.
Lighting and Weather Transformations
You can re-stage photos for different times of day, seasons, or weather conditions while preserving the core composition.
Make it look like [target conditions: winter evening with snowfall / sunny afternoon / foggy morning].
For precise control, use input_fidelity="high" in your API call. This preserves more of the original image's details while applying the environmental changes.
The model handles lighting direction, shadow angles, ground conditions (wet, snowy, dry), and atmospheric effects automatically based on your target description.
Object Removal
Removing unwanted elements from images is straightforward:
Remove the [specific element] from [location/subject]. Do not change anything else.
Examples from the guide include removing logos from clothing, removing design elements from objects, and changing specific colors or patterns.
The "do not change anything else" constraint is essential — without it, the model may take liberties with other parts of the image.
Inserting People into Scenes
For placing a person from one photo into an entirely different environment:
Generate a [realistic/specific style] scene where this person is [action] in [setting/location].
The image should look like a real photograph someone could have taken, not [unwanted style: overly enhanced, cinematic movie-poster].
[Additional context about pose, clothing, environment details].
[Lighting conditions] with natural lighting and realistic colors.
Everything should feel [grounded/authentic/unstyled], as if captured in a real moment.
Avoid [cinematic lighting/dramatic color grading/stylized composition].
For identity preservation in these complex edits, add input_fidelity="high" to your API call.
Multi-Image Compositing
When you need to combine elements from multiple input images into a single coherent result:
Place the [element] from [Image N] into the setting of [Image M], [position/placement details].
Use the same style of lighting, composition and background.
Do not change anything else.
Reference each input image by number and describe exactly what should transfer where. The model matches lighting and perspective when compositing.
Advanced Use Cases for Production Workflows
The guide includes several specialized applications that demonstrate the model's range.
Interior Design Visualization
For visualizing furniture or decor changes in real spaces:
In this room photo, replace ONLY [element to change] with [new element description].
Preserve camera angle, room lighting, floor shadows, and surrounding objects.
Keep all other aspects of the image unchanged.
Photorealistic contact shadows and [relevant material] texture.
The surgical precision here matters — you want to swap exactly one element without affecting the rest of the scene. Specifying shadows and lighting ensures the new element looks naturally integrated.
Product Concept Mockups
For seasonal marketing, merchandise concepts, or product visualization:
Create a [product type] of [subject description].
Concept:
[Brief description of the idea/theme/mood]
Style:
[Photography style], realistic [material] textures,
[lighting description],
[additional quality markers].
Constraints:
- Original design only
- No trademarks
- No watermarks
- No logos
Include ONLY this [text location] text (verbatim):
"[Your exact copy]"
This structure works for everything from holiday cards to collectible packaging to promotional materials.
Children's Book Illustration with Character Consistency
This is a sophisticated multi-step workflow for maintaining character appearance across multiple images.
Step 1: Establish your character anchor
Create a children's book illustration introducing a main character.
Character:
[Detailed character description including clothing, features, personality]
Theme:
[Story context]
Style:
[Art style description with technical details]
Constraints:
- Original character (no copyrighted characters)
- No text
- No watermarks
- Plain [setting] background to clearly showcase the character
This first generation becomes your reference image for all subsequent illustrations.
Step 2: Continue the story while maintaining consistency
Use the image editing endpoint with your character anchor image as input:
Continue the children's book story using the same character.
Scene:
[New scene description with specific action]
Character Consistency:
- Same [clothing items]
- Same facial features, proportions, and color palette
- Same [personality traits]
Style:
[Same style description as anchor]
Constraints:
- Do not redesign the character
- No text
- No watermarks
By referencing the anchor image and explicitly calling out consistency requirements, you can generate multiple illustrations that maintain a recognizable character throughout.
The Technical Setup (For Developers)
If you're building applications with GPT-image-1.5, here's the basic configuration from the guide.
Installation and setup:
import os
import base64
from openai import OpenAI
client = OpenAI()
# Create directories for your workflow
os.makedirs("input_images", exist_ok=True)
os.makedirs("output_images", exist_ok=True)
def save_image(result, filename: str) -> None:
"""Save the first returned image to the output folder."""
image_base64 = result.data[0].b64_json
with open(f"output_images/{filename}", "wb") as f:
f.write(base64.b64decode(image_base64))
Basic generation call:
result = client.images.generate(
model="gpt-image-1.5",
prompt=your_prompt
)
Generation with quality setting:
result = client.images.generate(
model="gpt-image-1.5",
prompt=your_prompt,
quality="high" # Use for detailed work, text-heavy images
)
Multiple variations:
result = client.images.generate(
model="gpt-image-1.5",
prompt=your_prompt,
n=4 # Generate 4 variations
)
Image editing:
result = client.images.edit(
model="gpt-image-1.5",
image=[
open("input_images/source.png", "rb"),
],
prompt=your_edit_prompt
)
Editing with multiple reference images:
result = client.images.edit(
model="gpt-image-1.5",
image=[
open("input_images/person.png", "rb"),
open("input_images/clothing_item1.png", "rb"),
open("input_images/clothing_item2.png", "rb"),
],
prompt=your_composite_prompt
)
High-fidelity editing for identity preservation:
result = client.images.edit(
model="gpt-image-1.5",
input_fidelity="high",
quality="high",
image=[
open("input_images/portrait.png", "rb"),
],
prompt=your_edit_prompt
)
Pricing and Access
GPT-image-1.5 is available now through two channels.
ChatGPT: The model is rolling out to all ChatGPT users — free tier included. There's a new dedicated "Images" interface in the ChatGPT sidebar that provides preset filters and trending prompts. Business and Enterprise access is coming later.
API: Available immediately for developers. Pricing is 20% lower than GPT-image-1 (the April 2025 model). Current rates are approximately $5 per million text tokens, $10 per million image input tokens, and $40 per million output tokens for generated images.
The speed improvements mean you can iterate more within the same budget. What used to be 10 generations might now give you 15-20 in the same processing time.
What I Actually Think (The Honest Take)
After spending a full day with GPT-image-1.5 and the prompting guide, here's my honest assessment.
The text rendering is a legitimate step forward. Generating infographics, posters, and UI mockups with readable text opens use cases that were essentially impossible before. I generated a 6-panel infographic with 40+ text labels, and every single one was legible.
The editing precision is real. Changing one element while preserving everything else actually works consistently now. Previous models would drift or reinterpret the whole image — this one follows "change only X" instructions reliably.
The speed matters more than I expected. When you're iterating on a design, going from 10-second generations to 3-second generations changes how you work. You experiment more. You try variations you wouldn't have bothered with before.
What still needs work:
Scientific and technical accuracy remains hit-or-miss. Asking for anatomically correct diagrams, accurate physics simulations, or precise mechanical drawings still produces errors.
Some artistic styles don't translate well. The guide acknowledges this — certain illustration approaches and fine art styles are inconsistently reproduced.
Complex multi-step workflows require careful prompt management. The model can drift across iterations, so you need to re-specify important constraints repeatedly.
Quick Reference: Prompt Templates You Can Copy
Infographic:
Create a detailed infographic explaining [topic].
Include [specific elements: diagrams, labels, data points].
Target audience: [specific audience].
Visual style: [clean/technical/friendly].
quality="high" for text clarity.
Photorealistic Portrait:
Create a photorealistic candid photograph of [subject with physical details].
Shot like a [film type] photograph, [framing] at [angle], using a [focal length] lens.
[Lighting], [depth of field], [color treatment].
The image should feel [authentic quality descriptors].
No glamorization, no heavy retouching.
Logo:
Create an original, non-infringing logo for [company name], a [business type].
Feel: [personality adjectives].
Use clean, vector-like shapes, strong silhouette, balanced negative space.
Favor simplicity over detail for readability at all sizes.
Flat design, minimal strokes.
Plain background, centered logo, generous padding.
No watermark.
Style Transfer:
Use the same style from the input image and generate [new subject] on [background].
Virtual Try-On:
Edit the image to dress [subject] using the provided clothing images.
Do not change face, facial features, skin tone, body shape, pose, or identity.
Preserve exact likeness, expression, hairstyle, proportions.
Replace only clothing with realistic fabric behavior.
Match lighting and shadows to original photo.
Do not change background, camera angle, or framing.
Object Removal:
Remove the [specific element] from [location]. Do not change anything else.
Weather/Lighting Change:
Make it look like [target conditions].
input_fidelity="high" for preservation.
UI Mockup:
Create a realistic [device] UI mockup for [product description].
Show [specific features/screens].
Design to be practical and usable.
[Color scheme], clear typography.
Place in [device frame].
Common Mistakes and How to Avoid Them
After going through dozens of generations and comparing successful prompts to failed ones, I've noticed several patterns in what goes wrong.
Mistake #1: Front-loading quality buzzwords instead of specifics.
Bad prompt
"Create an ultra-realistic 8K masterpiece stunning beautiful photograph of a woman in a garden"
Better prompt
"Photograph of a woman in her late 30s standing in an English cottage garden at golden hour. She wears a linen dress and holds a wicker basket. Shot at eye level with a 85mm lens, shallow depth of field focusing on her face. Warm afternoon light from camera-left, soft fill from sky. Film-like color grading with slightly lifted shadows."
The first prompt tells the model to be "good" without explaining what good looks like. The second prompt specifies every visual decision.
Mistake #2: Omitting constraints entirely.
When you don't tell the model what NOT to do, it improvises. And its improvisations often include watermarks, extra text, additional objects, or style elements you didn't want. Always include at minimum: "No watermark. No extra text. No logos." Add more specific exclusions based on your use case.
Mistake #3: Overloading a single prompt.
If your prompt is 500 words long describing every detail of a complex scene, you're asking for trouble. The model handles structured complexity better than dense paragraphs.
Break complex requests into: base scene → subject details → style constraints → exclusions. Use line breaks or short labeled sections.
Mistake #4: Using editing when you should be generating (and vice versa).
Generation creates images from text descriptions. Editing modifies existing images based on instructions. Use generation when: you want something new, you're exploring concepts, you don't have a reference image to work from.
Use editing when: you have an existing image you want to modify, you need to maintain consistency with previous work, you're doing style transfer or compositing.
Mistake #5: Not re-specifying constraints during iteration.
The model doesn't have perfect memory. If you generate an image, then ask for a modification in a follow-up prompt without restating important constraints, those constraints may be forgotten. Every iteration should include: what to change, what to preserve, and what to exclude. Yes, this means repeating yourself.
Mistake #6: Expecting scientific accuracy without verification.
The model has world knowledge, but it's not a physics simulator or an anatomy textbook. Technical diagrams, scientific illustrations, and mechanical drawings need human verification. Don't use AI-generated images as authoritative references for medical, scientific, or engineering content without expert review.
Wrap up
OpenAI didn't just release a better model. They released a manual for how to use it properly.
That's the actual breakthrough here. For years, AI image generation has felt like a lottery. Write a prompt, cross your fingers, hope the output matches what you imagined. The GPT-image-1.5 prompting guide changes that by documenting exactly which prompt structures, constraints, and workflows produce reliable results.
Is it perfect? No. The model has limitations, certain styles don't work well, and complex requests still require iteration. But the gap between "what I wanted" and "what I got" is smaller than it's ever been.
The full guide is available at cookbook.openai.com. I'd recommend bookmarking it and returning whenever you're trying a new use case. The prompt patterns are worth internalizing.
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