Hoe je AI-gegenereerde afbeeldingen er echt uit laat zien: stap-voor-stap handleiding

If an AI image looks fake, the problem is usually not one dramatic mistake. It is a stack of small signals: soft plastic skin, shadows that do not agree, props with strange scale, text that almost works, or a camera angle that no real lens would produce. The fix is also a stack: start with a grounded scene, give the model visual constraints, judge the first result like a photo editor, then clean up only the parts that break believability.

This workflow was refreshed on July 30, 2026 against current image-generation documentation from OpenAI en Google Tweelingen, plus guidance from Midjourney, C2PA, and Google Search Central. The simple point is this: photorealism comes from controlling the whole image pipeline, not from adding the word “realistic” to a prompt.

If you want to compare a few image models before committing to one look, GlobalGPT’s image generator is a practical place to test the same scene across different workflows. Use it as a comparison workspace: find the model that handles your subject, lighting, and edits best, then still apply the realism checks below before publishing.

Alles-in-één AI-platform voor schrijven, afbeeldingen en video's maken met GPT-5, Nano Banana en meer

Quick Answer: How Do You Make AI Images Look Real?

Make AI generated images look real by describing a plausible photo, not a perfect image. Specify the subject, environment, lens, lighting direction, material details, and what should stay imperfect. Use a reference image when pose, layout, product shape, or brand consistency matters. After generation, inspect the result for hands, eyes, reflections, shadows, scale, repeated patterns, background objects, and text. Then edit locally instead of regenerating the entire picture after every small flaw.

Why AI Images Still Look Fake

Most weak AI photos fail because they are too smooth and too convenient. A real product shot has tiny dust, uneven fabric, compressed shadows, fingerprints, imperfect reflections, and a background that follows the same light as the subject. A fake-looking image often has the opposite: everything is equally sharp, equally polished, and equally lit.

The newer image models are better at following detailed instructions, but they still need a realistic frame. OpenAI’s image documentation now centers generation and editing around current image models such as GPT afbeelding 2, while Google’s image-generation docs describe Nano Banana models for generation and editing. That makes the tool stronger, but the editor still has to decide what a believable photo should contain.

The AI Image Realism Checklist

Before you regenerate, run the image through a short photo-editor checklist. This saves time because it separates prompt problems from small repair problems.

Fix It Locally or Regenerate?

If you are making product images, this checklist pairs well with a product-specific workflow like creating realistic product images with Nano Banana, because product realism depends heavily on scale, labels, materials, and shadow contact.

A Prompt Formula That Makes AI Photos More Believable

A strong realism prompt should read like a compact art direction brief. It should tell the model what a real photographer would need to know, then leave room for ordinary imperfection.

Use This Structure

1. Betreft: who or what is in the image.
2. Scène: where it is, what time of day, what is nearby.
3. Camera: lens, camera height, framing, depth of field.
4. Light: source, direction, softness, shadow behavior.
5. Materials: skin, fabric, wood, metal, glass, food, packaging.
6. Imperfections: subtle grain, uneven texture, small dust, natural wrinkles.
7. Exclusions: no warped hands, no floating objects, no unreadable text, no plastic skin.

The important part is not the exact words. It is the order of detail. Start with the real-world scene, then add camera and light, then add the defects that make the picture feel less synthetic. For more prompt patterns, use the Handleiding voor de Nano Banana-prompt as a deeper prompt library.

Detailed Prompt vs. Vague Prompt

A detailed prompt gives the model more useful constraints; it does not guarantee a photorealistic result. The comparison below shows the practical difference. The more specific brief creates a coherent room with directional light and recognizable materials, while the vague brief falls back to a stylized interpretation. Both images still need a close inspection before use.

Use Reference Images Without Copying Blindly

Reference images are best for structure, not for shortcuts. Use them when you need a consistent face, a product shape, a room layout, a pose, or a color palette. Do not expect a reference to fix an unclear brief. If the source image has bad light or awkward composition, the generated image may inherit those problems.

A cleaner workflow is to give the model one strong reference and one written brief. The reference controls shape and composition; the prompt controls lighting, camera, realism, and exclusions. This matters for image editing models because current tools increasingly support both generation and editing from images. If your workflow is more about changing an existing picture than starting from a blank prompt, the guide to editing images with ChatGPT is a useful next stop. Google describes Gemini image generation with Nano Banana and Nano Banana Pro, while OpenAI’s image workflow also supports editing and generated outputs from supplied images.

Lighting and Camera Settings That Sell the Image

Lighting is where many AI images give themselves away. Avoid prompts that ask for “cinematic lighting” without direction. Name the light source, the side it comes from, and how hard or soft the shadow should be.

Do not overload lens language. A prompt that says “8K, DSLR, cinematic, 24mm, 50mm, 85mm, macro, bokeh” gives the model conflicting instructions. Pick one real visual direction and stay with it.

Choosing the Right Tool for Realistic AI Images

The best tool depends on what realism means in your task. A fashion image, product packshot, editorial portrait, social ad, and meme all have different failure points. The table below is a practical choice guide, not a universal ranking.

If your article, ad, or product page depends on current model access, check the provider page the day you publish. Model names and output rules move faster than evergreen prompt advice.

Post-Processing: The Step Most Beginners Skip

The most realistic AI images usually have a cleanup pass. That does not mean heavy editing. It means fixing the few things a viewer will notice first.

A Simple Editing Order

1. Crop first, because composition changes how flaws read.
2. Fix exposure and white balance before adding style.
3. Repair hands, eyes, text, labels, and hard edges.
4. Add light grain or texture only if the image feels too clean.
5. Sharpen selectively, especially around the subject, not the whole image.
6. Export at the size you actually need, then inspect it at 100%.

For small web graphics, an AI upscaler or sharpening pass can help, but it cannot repair a bad scene. If the image is part of a repeat editing workflow, compare the broader AI tools for photo editors after the main realism issues are already solved.

Commercial Use, Disclosure, and Provenance

Realistic AI images create a trust problem if the viewer may reasonably assume the image is documentary. For product mockups, ads, editorial images, or social campaigns, check three things before publishing: the tool’s commercial terms, whether the image could mislead people, and whether provenance or disclosure is needed in your context.

C2PA and Content Credentials exist because viewers, publishers, and platforms increasingly need a way to understand where media came from. Google Search’s guidance on AI-generated content also focuses on helpful, people-first quality rather than treating automation as the only issue. In practice, that means your realistic AI image should support the page honestly, not fake evidence that never existed.

For a deeper rights check, use the companion guide on using AI images commercially. That topic deserves its own check because licenses, subscriptions, and platform rules can change.

Common Mistakes That Make AI Images Look Synthetic

• Using style words instead of visual facts: “photorealistic” is weaker than “single window light from left, contact shadow on table.”
• Asking for too many aesthetics at once: cinematic, editorial, macro, product, documentary, and fashion cues can fight each other.
• Ignoring scale: plates, cups, hands, furniture, and packaging often look wrong when no real measurement is implied.
• Trusting generated text: add important labels, logos, and legal copy manually.
• Over-smoothing skin: real skin has pores, small color variation, and asymmetry.
• Publishing the first good-looking result: inspect at full size before the image goes into an ad, product page, or article hero.

Realistic Prompt Examples by Use Case

If you are building a broader image workflow, compare this with ChatGPT alternatives for images so the model choice matches the visual job instead of the other way around.

FAQ

How do I make AI generated images look less fake?

Start by fixing light, scale, and texture. Give the image one clear light source, a realistic camera angle, believable material details, and natural imperfections. Then inspect hands, eyes, text, reflections, and background objects before publishing.

What prompt makes AI images look realistic?

A realistic prompt describes the scene like a photo brief: subject, setting, camera, lens, light direction, material texture, imperfections, and exclusions. For example, “50mm lens, window light from the left, soft contact shadow, visible fabric weave, no warped hands, no unreadable text” is stronger than “make it realistic.”

Which AI image generator is best for photorealism?

There is no single best tool for every case. GPT Image 2, Nano Banana Pro, Midjourney, and Stable Diffusion-style workflows can all be useful depending on whether you need prompt following, editing, visual taste, or local control. Check current provider documentation before making claims about access, pricing, or output limits.

Should I use reference images?

Yes, when structure matters. Reference images help with pose, product shape, room layout, color palette, and consistency. Pair the reference with a written prompt so the model knows what to keep and what to change.

Can AI images be used commercially?

Often yes, but it depends on the tool, subscription, license, subject matter, and publishing context. Check the provider’s commercial terms and avoid using realistic AI images in a way that could mislead viewers about real events, people, or products.

Final Workflow

1. Write the scene as a real photo brief.
2. Add one camera and one lighting setup.
3. Use a reference image when pose, layout, or product shape matters.
4. Generate several versions and choose the least broken one, not only the prettiest one.
5. Repair local flaws instead of restarting the whole image.
6. Check rights, disclosure, and provenance before commercial use.

The image should not look like a model showing off. It should look like a photo that had a reason to exist.