AI Ad Prompt Guide

Universal prompting framework for AI video and image generation — works across all major models.

Part 1: Universal Prompting Rules (Standalone)

1.1 The SLCT Framework

Every effective AI generation prompt has four components. Use SLCT as a checklist:

S — Subject: What is the main focus? L — Lighting/Look: What's the visual mood? C — Camera: What angle, movement, and framing? T — Technical: Resolution, aspect ratio, duration, style keywords?

SLCT Examples

Product B-roll:

S: A glass bottle of amber serum on a marble bathroom counter
L: Soft golden morning light streaming from the left, creating gentle highlights on the glass
C: Slow push-in from medium to close-up, shallow depth of field
T: 4K, 5 seconds, photorealistic, product photography style

UGC-style:

S: A woman in her late 20s opening a package on her couch, looking excited
L: Natural indoor lighting, warm tones, slightly imperfect like a phone camera
C: Medium shot, handheld slight shake, selfie-style front camera angle
T: 9:16 vertical, 5 seconds, realistic, casual home setting

Cinematic hero shot:

S: A sleek electric car driving along a coastal highway at sunset
L: Dramatic golden hour, long shadows, warm highlights on the car body
C: Low-angle tracking shot from the front quarter, smooth dolly movement
T: 16:9, 8 seconds, cinematic, film grain, anamorphic lens flare

1.2 Hallucination Prevention

AI models hallucinate when prompts are ambiguous, contradictory, or physically impossible. These rules minimize bad outputs:

The 5 Rules of Hallucination Prevention

1. Be spatially explicit: "A bottle on the LEFT side of a marble counter, a plant on the RIGHT" — not "a bottle near a plant on a counter"
2. Limit entities: Maximum 3 main subjects per scene. More = more chance of merging/distortion
3. Avoid negatives: Don't say "no people in the background" — instead describe what IS there: "empty cafe with wooden chairs"
4. Use real-world references: "lighting like a Vogue cover shoot" anchors the model better than "beautiful professional lighting"
5. Specify quantities: "Two coffee cups" not "coffee cups". "A single person" not "a person" (which might generate multiple)

Common Hallucination Triggers & Fixes

1.3 Camera Movement Vocabulary

Use these precise terms — AI models understand film terminology better than casual descriptions.

Camera Angle Vocabulary

1.4 UGC Prompt Formulas

These templates generate authentic-feeling content that doesn't look "AI generated."

Unboxing Formula

A [age] [gender] sitting [location], opening a [color] package.
[Lighting]: Natural [time of day] light from a nearby window, warm tones.
[Camera]: Medium close-up, slightly shaky handheld, phone camera quality.
[Expression]: Genuine surprise and excitement.
[Duration]: 5 seconds.
[Style]: Realistic, casual, user-generated content aesthetic.

Product Review Formula

A [age] [gender] looking directly at camera, holding up a [product].
[Setting]: [Casual home location — kitchen, bathroom, living room].
[Lighting]: Natural indoor light, not studio-perfect.
[Camera]: Front-facing selfie angle, slight phone tilt, 9:16 vertical.
[Expression]: Enthusiastic, conversational, making eye contact.
[Duration]: 5 seconds.
[Style]: Authentic UGC, not polished commercial.

Lifestyle / Day-in-the-Life Formula

A [age] [gender] using [product] during their [morning/evening] routine.
[Setting]: [Realistic home environment].
[Lighting]: Soft natural light, golden hour warmth through windows.
[Camera]: Medium shot following the action, gentle handheld movement.
[Action]: [Specific natural action — applying, pouring, wearing].
[Duration]: 5 seconds.
[Style]: Lifestyle photography, editorial casual.

1.5 Product Shot Techniques

Hero Product Shot

A [product] centered on a [surface], [environment context].
[Lighting]: [Dramatic/soft/natural] studio lighting with [specific direction].
[Background]: [Clean/textured/contextual] — [specific description].
[Camera]: [Static macro / slow orbit / push-in] with shallow depth of field.
[Props]: [1-2 complementary items that add context without competing].
[Duration]: 5 seconds.
[Style]: High-end product photography, [brand mood — luxurious/minimal/vibrant].

Before/After Product Shot

Split composition: LEFT side shows [before state], RIGHT side shows [after state].
[Transition]: Smooth wipe or morph from left to right over 3 seconds.
[Lighting]: Even, clean lighting to show detail in both states.
[Camera]: Static, locked-off shot. Centered framing.
[Duration]: 5 seconds.
[Style]: Clean, medical/scientific feel OR dramatic transformation.

1.6 The Pass³ Quality Test

Before using any AI-generated asset in an ad, run it through this 3-pass test:

Pass 1 — Physics Check (2 seconds)

• Do objects obey gravity?
• Are reflections correct?
• Do shadows match light sources?
• Are proportions realistic?

Pass 2 — Detail Check (5 seconds)

• Hands: correct number of fingers, natural poses?
• Text: readable or garbled? (If garbled, plan to overlay in post)
• Faces: symmetrical, natural expressions?
• Edges: clean boundaries between objects?

Pass 3 — Brand Check (3 seconds)

• Does the lighting match your brand mood?
• Is the color palette on-brand?
• Would this fit on your website/social feed without looking out of place?
• Could a viewer tell this is AI? (For UGC, "slightly imperfect" is fine)

Decision: If it fails any pass, regenerate with an adjusted prompt. Don't fix bad generations in post — it's faster to regenerate.

Part 2: Model-Specific Guidance

2.1 Model Selection Decision Matrix

2.2 Model-Specific Prompting Tips

Sora 2

• Excels at: Smooth camera movements, consistent lighting, coherent scenes
• Struggles with: Fine text, complex multi-person interactions
• Tip: Use descriptive scene-setting language. Sora responds well to cinematic terminology.
• Duration options: 4s, 8s, 12s
• Cost-effective for rapid iteration at standard quality

Veo 3.1

• Excels at: Cinematic quality, coherent long sequences, good physics
• Struggles with: Sometimes overly "cinematic" when you want casual
• Tip: For UGC, explicitly state "phone camera quality, not cinematic"
• Duration options: 4s, 6s, 8s
• Best-in-class for hero content and brand videos

Kling 2.1 Master

• Excels at: Image-to-video with motion, product animations, face consistency
• Struggles with: Can be slower, higher cost
• Tip: Provide a high-quality reference image for best results. Use cfg_scale 0.3-0.5 for creative freedom, 0.7-0.9 for prompt adherence.
• Duration options: 5s, 10s

Nano Banana

• Excels at: Text rendering in images, product photography, logo fidelity
• Struggles with: Only generates images (not video)
• Tip: Best for generating product shots that will be used as reference images for image-to-video models.
• Use nano-banana/edit for image-to-image modifications

Flux Pro

• Excels at: Image editing, style transfer, multi-image compositing
• Struggles with: Less creative freedom than pure generation models
• Tip: Use kontext/text-to-image for generation, kontext/max/multi for editing existing images with new elements.

Seedance v1 Pro

• Excels at: Human motion, dance movements, natural body language
• Struggles with: Non-human subjects
• Tip: Best for UGC and avatar-style content where natural movement matters.
• Duration options: 5s, 10s at 480p/720p/1080p

Part 3: API Automation

These models are available through a unified Asset Generator API, which provides a single endpoint for all models.

3.1 Asset Generator — Unified Access

import requests

HEADERS = {
    "Content-Type": "application/json",
    "X-API-ID": "your-api-id",
    "X-API-KEY": "your-api-key",
}
BASE_URL = "https://api.creatify.ai/api"

def get_model_schemas(model_name=None):
    """Discover available models and their input parameters."""
    url = f"{BASE_URL}/asset_generator/schemas/"
    if model_name:
        url += f"?model_name={model_name}"
    resp = requests.get(url, headers=HEADERS)
    resp.raise_for_status()
    return resp.json()

def generate_asset(model_name, input_params, webhook_url=None):
    """Generate an image or video using any available model."""
    payload = {
        "model_name": model_name,
        "input_params": input_params,
    }
    if webhook_url:
        payload["webhook_url"] = webhook_url

    resp = requests.post(f"{BASE_URL}/asset_generator/", headers=HEADERS, json=payload)
    resp.raise_for_status()
    return resp.json()

def check_generation_status(generation_id):
    """Check status of an asset generation job."""
    resp = requests.get(f"{BASE_URL}/asset_generator/{generation_id}/", headers=HEADERS)
    resp.raise_for_status()
    return resp.json()

Don't have an API key yet? No problem — grab one in under 2 minutes:

1. Sign up free at creatify.ai
2. Go to Settings → API
3. Copy your API ID and API Key — that's it. New accounts get free credits to start.

3.2 Quick Examples

Generate a product image (Nano Banana)

result = generate_asset(
    model_name="nano-banana",
    input_params={
        "prompt": "A glass bottle of amber face serum on a white marble counter, soft studio lighting, product photography, clean background, 4K detail",
    }
)

Generate B-roll video (Sora 2)

result = generate_asset(
    model_name="sora-2/text-to-video",
    input_params={
        "prompt": "Slow push-in on a coffee cup on a wooden table in a cozy cafe, morning sunlight streaming through the window, steam rising from the cup, shallow depth of field, cinematic",
        "duration": "4",
    }
)

Image-to-video product animation (Kling)

result = generate_asset(
    model_name="kling-video/v2.1/master/image-to-video",
    input_params={
        "prompt": "Slow orbit around the product, dramatic lighting, product showcase",
        "image_url": "https://example.com/product-photo.jpg",
        "duration": "5",
        "aspect_ratio": "9:16",
        "cfg_scale": 0.5,
    }
)

3.3 Credit Costs Reference

3.4 Recipe: Image → Video B-Roll Pipeline

Generate a product photo first, then animate it.

import time

def image_to_video_broll(image_prompt, video_prompt, video_model="kling-video/v1.6/pro/image-to-video"):
    """Pipeline: generate image → animate into video B-roll."""

    # Step 1: Generate product image
    image_job = generate_asset(
        model_name="nano-banana",
        input_params={"prompt": image_prompt}
    )

    # Step 2: Poll for image
    while True:
        status = check_generation_status(image_job["id"])
        if status["status"] == "done":
            image_url = status["assets"][0]["url"]
            break
        elif status["status"] in ("failed", "error"):
            raise Exception(f"Image gen failed: {status.get('failed_reason')}")
        time.sleep(5)

    # Step 3: Animate image into video
    video_job = generate_asset(
        model_name=video_model,
        input_params={
            "prompt": video_prompt,
            "image_url": image_url,
            "duration": "5",
            "aspect_ratio": "9:16",
        }
    )

    # Step 4: Poll for video
    while True:
        status = check_generation_status(video_job["id"])
        if status["status"] == "done":
            return status["assets"][0]["url"]
        elif status["status"] in ("failed", "error"):
            raise Exception(f"Video gen failed: {status.get('failed_reason')}")
        time.sleep(10)

See Also

• video-ad-generator — Product URL → video ad pipeline
• ai-avatar-video — AI talking-head videos with 1,500+ personas
• ad-creative-evaluator — Score any video ad with AI expert panel
• video-ad-reverse-engineer — Reverse-engineer competitor ads
• static-ad-concept-generator — 320+ proven ad concept templates