Photography AI

Photography AI - Professional Visual Engineering Skills Framework

A comprehensive, structured reference for AI-powered visual creation, covering prompt engineering, photographic literacy, strategic negation, identity preservation, post-processing, and agent orchestration.

Version: 3.0 | Last Updated: April 2026

Context

This skill is for anyone who uses AI to generate, refine, or orchestrate visual content. It treats AI image/video generation as a professional engineering discipline: systematic, physics-informed, quality-gated, and continuously improvable.

Use this skill when:

• Generating photorealistic or stylized images with AI
• Creating cinematic video sequences from text prompts
• Building multi-step visual production pipelines
• Designing AI agent workflows for creative teams
• Troubleshooting AI generation artifacts (skin, hands, anatomy)

Instructions

Step 1: Understand the Skill Synergy Map

Skills compound. The six categories build on each other:

FOUNDATION
  Technical Prompt Engineering + Photographic Literacy
       |                     |
       v                     v
CONSISTENCY LAYER      REFINEMENT LAYER
  Strategic Negation    Post-Processing &
  + Identity Preserv.     Hybrid Workflows
       |                     |
       v                     v
         ORCHESTRATION LAYER
      AI Agent Design + Production Deploy

Step 2: Apply Technical Prompt Engineering

Structure prompts as blueprints, not keyword lists. Follow the Scaffold Method:

[Subject] + [Action] + [Lighting] + [Lens/Specs] + [Style] + [Quality]

Rules:

• Front-load critical elements (AI weights early tokens more heavily)
• Use precise photographic vocabulary over vague buzzwords
• Use active voice for iterative edits ("remove the background", "add a red hat")
• Stack semantic concepts in deliberate order to control interpretation hierarchy

Step 3: Apply Photographic Literacy

Use real-world physics terminology:

Step 4: Apply Strategic Negation

Tell the AI what NOT to include:

PROMPT:  visible pores, fine vellus hair, subtle skin variation
NEGATE:  (plastic skin:1.4), (airbrushed:1.2), (cartoon:1.3)

Step 5: Maintain Identity Preservation

For multi-generation consistency:

1. Seed locking: Fix initial noise pattern with --seed 12345
2. Reference tools: Use --cref (character) and --sref (style) references
3. Character weight: --cw 80 preserves face + clothing

Step 6: Post-Processing Workflow

1. Iterative refinement: Keep seed, change one variable at a time
2. Inpainting: Fix errors (hands, eyes) via targeted masked editing
3. External enhancement: Upscale with Topaz, color grade
4. Quality checklist: hands/feet anatomy, eye direction, lighting coherence

Constraints

• NEVER treat AI generation as final output -- always plan for post-processing
• NEVER skip the negative prompt step -- uncontrolled generation produces artifacts
• NEVER use vague buzzwords when technical terms exist
• NEVER forget to test at the target platform's native resolution
• NEVER generate character series without seed locking or reference tools

Examples

Example 1: Professional Headshot

PROMPT: Corporate headshot of a CEO, confident expression with subtle warmth, corner lighting establishing authority, dark navy suit against library background, 85mm f/2.8 shallow depth of field, photorealistic, 4K native resolution

NEGATE: (plastic skin:1.4), (airbrushed:1.3), (symmetrical face:1.1), (cartoon:1.2)

Example 2: Cinematic Video Scene

PROMPT: Protagonist discovers crucial clue in dim library, camera: slow dolly zoom from wide establishing shot to tight close-up, lighting: golden hour backlight through window with practical desk lamp fill, 24fps 4K native ProRes

NEGATE: (facial drift:1.4), (background flicker:1.3), (inconsistent props:1.2)