AI Artist - Prompt Engineering
Craft effective prompts for AI text and image generation models.
Core Principles Clarity - Be specific, avoid ambiguity Context - Set scene, role, constraints upfront Structure - Use consistent formatting (markdown, XML tags, delimiters) Iteration - Refine based on outputs, A/B test variations Quick Patterns LLM Prompts (Claude/GPT/Gemini) [Role] You are a {expert type} specializing in {domain}. [Context] {Background information and constraints} [Task] {Specific action to perform} [Format] {Output structure - JSON, markdown, list, etc.} [Examples] {1-3 few-shot examples if needed}
Image Generation (Midjourney/DALL-E/Stable Diffusion) [Subject] {main subject with details} [Style] {artistic style, medium, artist reference} [Composition] {framing, angle, lighting} [Quality] {resolution modifiers, rendering quality} [Negative] {what to avoid - only if supported}
Example: Portrait of a cyberpunk hacker, neon lighting, cinematic composition, detailed face, 8k, artstation quality --ar 16:9 --style raw
References
Load for detailed guidance:
Topic File Description LLM references/llm-prompting.md System prompts, few-shot, CoT, output formatting Image references/image-prompting.md Style keywords, model syntax, negative prompts Nano Banana references/nano-banana.md Gemini image prompting, narrative style, multi-image input Advanced references/advanced-techniques.md Meta-prompting, chaining, A/B testing Domain Index references/domain-patterns.md Universal pattern, links to domain files Marketing references/domain-marketing.md Headlines, product copy, emails, ads Code references/domain-code.md Functions, review, refactoring, debugging Writing references/domain-writing.md Stories, characters, dialogue, editing Data references/domain-data.md Extraction, analysis, comparison Model-Specific Tips Model Key Syntax Midjourney --ar, --style, --chaos, --weird, --v 6.1 DALL-E 3 Natural language, no parameters, HD quality option Stable Diffusion Weighted tokens (word:1.2), LoRA, negative prompt Flux Natural prompts, style mixing, --guidance Imagen/Veo Descriptive text, aspect ratio, style references Anti-Patterns Vague instructions ("make it better") Conflicting constraints Missing context for domain tasks Over-prompting with redundant details Ignoring model-specific strengths/limits