AI Image Generation Prompt Engineering

Professional Techniques: White Pages

Edition: January 2026

Preface

This is designed as a comprehensive training resource for professionals seeking to master prompt engineering in AI image generation systems. Drawing from established best practices as of early 2023, updated for 2026, it covers essential techniques across leading models. The content emphasizes structured learning, with each chapter including theoretical explanations, templates, advanced prompt examples, and practical skill assignments to reinforce understanding.

By completing the assignments, learners will develop hands-on proficiency in crafting prompts that yield high-quality, consistent results. Prerequisites include access to the relevant AI tools (e.g., Flux, Midjourney, Stable Diffusion, Gemini AI, CapCut) and a basic familiarity with image generation interfaces.

Chapter 3: Nano Banana & Nano Banana Pro (Gemini 2.5 Flash Image / Gemini 3 Pro Image)

Focused on Google’s Gemini ecosystem, this chapter highlights techniques for models excelling in logical reasoning, text integration, and factual accuracy.

Key Strengths (2026):

Superior reasoning and logical consistency.
Exceptional text rendering and multi-language legibility.
Strong real-world knowledge and factual grounding.
Ideal for infographics, branded assets, mockups, diagrams, and consistent identity.
Supports multi-image input and native editing.

Optimized Prompt Structure (structured narrative or explicit instructions):

[Clear task directive: Generate / Create / Design / Edit / Transform], 

a [main subject with strong identity details — age, ethnicity, clothing, expression], 

performing [specific action / pose / interaction],

placed in [detailed, spatially logical environment with clear depth layers and secondary objects],

arranged in [explicit composition: symmetrical, rule-of-thirds, grid, centered header, 3×3 layout, etc.],

rendered in [precise style / medium: photorealistic product photography, clean vector infographic, editorial portrait, modern UI mockup],

utilizing [lighting: soft 5600K daylight from 45° upper left, dramatic volumetric god rays, studio key + fill],

with [color palette + mood + grading],

[technical mandates: ultra-sharp focus, 4K–8K resolution, maximum text legibility, accurate proportions, no anatomical errors, factual correctness],

[hard constraints: exact text placement and wording “Bold sans-serif centered title: 2026 Technology Trends”, consistent character identity across angles, no UI elements from design software]

[reference instructions if using multi-image: maintain style from reference 1, pose from reference 2, color grading from reference 3]

Pro Tip: For the Pro version, incorporate “Reason step-by-step about composition, lighting balance, and text placement before generating.”

Advanced Prompt Examples

These showcase reasoning integration, text-heavy designs, and multi-reference use:

Infographic with Reasoning: “Design a clean vector infographic timeline of quantum computing milestones from 1980 to 2026, arranged in a 3×3 grid layout with icons for each event, rendered in modern flat design style, utilizing soft 5600K daylight lighting, with blue-green palette and professional grading, ultra-sharp 4K resolution, maximum text legibility, factual accuracy based on real historical data. Exact text placement: bold sans-serif centered header ‘Quantum Computing Evolution’, no distortions. Reason step-by-step about icon placement and color balance before generating.”
Branded Asset Edit: “Transform reference image 1 (corporate logo) into a branded mockup of a 40-year-old executive presenting a quarterly report, consistent identity from reference 2, placed in a modern conference room with depth layers including audience and projector screen, symmetrical composition, editorial portrait medium, studio key light from upper left, neutral tones with brand blue accents, 8K resolution, accurate anatomy. Hard constraints: include exact text ‘Q4 2026 Results’ on screen, no background clutter.”

Practice Skill Assignments

Logical Prompt Building: Design an infographic prompt for “AI Evolution Timeline” using the structure. Include factual constraints and text placement. Generate in Nano Banana Pro and assess legibility.
Multi-Image Consistency: Upload two reference images. Craft a prompt to blend elements while maintaining identity. Generate a series of consistent angles and evaluate coherence.
Editing Workflow: Start with a base generation. Use editing directives (e.g., “Remove background, add text overlay”) in subsequent prompts. Document the iterative improvements.

Addendum: Glossary of Terms, Acronyms, and Abbreviations

This addendum provides definitions for key terms, acronyms, and abbreviations used throughout the textbook. Each entry includes a concise definition and an example of use in the context of AI image generation prompt engineering. Entries are listed alphabetically for ease of reference.

–ar (Aspect Ratio): A model-specific parameter used in tools like Midjourney or Stable Diffusion to define the width-to-height ratio of the generated image.
Example: In a prompt, “–ar 16:9” ensures the output is widescreen, suitable for cinematic landscapes.
Aspect Ratio: The proportional relationship between the width and height of an image, often specified to control composition.
Example: Selecting “16:9” in CapCut’s UI for video-friendly social media visuals.
CGI (Computer-Generated Imagery): Digital visuals created using computer software, often mimicking real-world appearances.
Example: “Realistic CGI style” in a prompt to generate lifelike space scenes.
Cinematic: A style evoking film aesthetics, including dramatic lighting, composition, and depth.
Example: “Cinematic composition from a low-angle 16mm lens” to create epic, movie-like fantasy images.
Composition: The arrangement of visual elements within an image frame, such as rule-of-thirds or symmetrical layouts.
Example: “Symmetrical composition” in Nano Banana prompts for balanced infographics.
Constraints: Explicit rules or limitations in a prompt to guide the AI and prevent unwanted outputs.
Example: “No distortions, accurate anatomy” to ensure realistic human figures.
Depth of Field: A photographic effect where only part of the image is in sharp focus, blurring foreground or background.
Example: “Cinematic depth of field” in CapCut prompts to emphasize subjects in portraits.
Diffusion Models: AI architectures (e.g., Stable Diffusion) that generate images by iteratively denoising random data.
Example: Using Flux or SD family models for detailed artistic freedom in photorealism.
Factual Grounding: Ensuring generated content aligns with real-world knowledge or data.
Example: In Nano Banana, “Factual accuracy based on real historical data” for educational timelines.
God Rays: Volumetric light beams piercing through atmosphere, creating dramatic effects.
Example: “Dramatic volumetric god rays” in prompts for epic fantasy environments.
HDR (High Dynamic Range): Imaging technique capturing a wide range of light intensities for more realistic contrasts.
Example: “High dynamic range lighting” to enhance realism in outdoor scenes.
Image-to-Image: A generation mode where an input image is transformed based on a prompt.
Example: In CapCut, “Transform this photo into anime style” for style shifts.
Infographic: A visual representation of information or data, often using charts, icons, and text.
Example: Nano Banana prompts for “Clean vector infographic timeline” in branded assets.
Inpainting/Outpainting: Editing techniques to fill in or extend specific image areas.
Example: Flux/SD editing strength for refining generated portraits.
Iteration: The process of refining prompts through successive modifications and generations.
Example: “Iterate by changing only the lighting descriptor” in practice assignments.
Midjourney: A diffusion-based AI image generator known for cinematic and artistic outputs.
Example: Using “–stylize 750 –v 7” parameters for masterpiece-level renders.
Nano Banana: Codename for Google’s Gemini advanced image generation models, emphasizing reasoning and text fidelity.
Example: “Reason step-by-step about composition” in Pro version prompts.
Negative Prompt: A list of elements to exclude from the generated image to avoid flaws.
Example: “Blurry, lowres, deformed” in Stable Diffusion to improve quality.
Photorealistic: A style mimicking real photographs with high detail and accuracy.
Example: “Photorealistic product photography” for professional mockups.
Prompt Engineering: The craft of designing effective text inputs to guide AI models in generating desired outputs.
Example: Structuring prompts logically for optimal AI image results.
Prompt Weight: A mechanism (e.g., (1.2)) to emphasize or de-emphasize elements in a prompt.
Example: “Majestic ancient dragon (1.2)” to prioritize the subject’s detail.
Reasoning: The AI’s step-by-step logical processing, prominent in models like Nano Banana Pro.
Example: “Reason step-by-step about icon placement” for consistent designs.
Rule-of-Thirds: A composition guideline dividing the frame into thirds for balanced placement of elements.
Example: “Rule-of-thirds composition” in prompts for dynamic portraits.
SD (Stable Diffusion): An open-source diffusion model family for text-to-image generation.
Example: Medium-long detailed prompts for artistic photorealism.
Specificity: The level of detail in descriptors to achieve precise AI outputs.
Example: “Highly specific physical traits, age, ethnicity” in universal templates.
–stylize: A Midjourney parameter controlling artistic abstraction level.
Example: “–stylize 750” for highly stylized cinematic masterpieces.
Text Rendering: The AI’s ability to generate clear, legible text within images.
Example: “Maximum text legibility” in Nano Banana for infographics.
UI (User Interface): The graphical elements through which users interact with software.
Example: CapCut’s “UI style selector” for choosing anime or trending categories.
–v (Version): A parameter specifying the model version in tools like Midjourney.
Example: “–v 7” to access the latest features for improved outputs.
Vector: A scalable graphic format using paths, ideal for clean designs.
Example: “Clean vector infographic” in Nano Banana for diagrams.
Volumetric Lighting: Light simulation accounting for atmospheric scattering and volume.
Example: “Dramatic volumetric teal-pink lighting” in CapCut for moody scenes.