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A type of prompt designed to simulate autonomous behavior, often used in multi-step logic chains or task automation.
A foundational prompt that sets the tone, context, and style for a session or series. Example: “You are a creative AI working alongside a professional prompt designer. All outputs should be elegant, technically rich, and visionary. First, describe a future city in 150 words.” This Anchor Prompt defines voice, tone, and purpose from the start.
When an AI confidently delivers false or fabricated content. Example: Prompt: ‘summarize Einstein’s research on time travel in his 1998 essay.” The AI might generate a realistic-sounding summary — but Einstein never wrote such a paper.A hallucination with poetic flair.
A visual reference tag in image prompts used to simulate styles like “Baroque” or “Anime”. Example: Prompt: “Portrait of a 1930s jazz singer, oil painting, in the style of Norman Rockwell.” “In the style of“ is the key Art Style Tag that shapes color, brushstroke, and composition.
The proportional relationship between width and height in image generation. Example: Prompt: “Cyberpunk cityscape, 16:9 aspect ratio, ultrawide angle, cinematic shot.” The aspect ratio influences framing and mood — from square to epic.
Combining prompts with external data or context to enrich the output. Example: “Based on the user’s uploaded resume, generate a professional cover letter tailored to a software engineering job in San Francisco.” The AI now builds on user-specific data, not just language rules.
Introducing unintended cultural, political, or emotional bias into a prompt. Example: Prompt: “Create a portrait of a powerful leader.” The AI may default to stereotypical imagery unless bias is intentionally balanced (e.g. “powerful leader – Indigenous woman in modern armor”).
A core prompt used as a repeatable starting point for multiple variations. Example: Base Prompt: “Close-up fantasy portrait of a warrior queen, golden hour lighting, hyper-detail.” Use this structure again and again, swapping only small elements for consistent quality.
Creating several outputs from one prompt, typically for comparison or refinement. Example: Prompt: “Design futuristic sneakers for urban explorers.” Batch generation produces 4+ versions — a visual brainstorming session with AI as co-creator.
A creative fusion of two or more ideas into one output—useful for hybrid styles. Example: Prompt: “An astronaut wizard riding a dragon made of solar flares, in Renaissance oil painting style.” The blend crosses genres, visuals, and eras in one line.
Using a series of prompts where each one builds on the previous to guide complex tasks. Example: Prompt 1: “Describe a futuristic city ruled by plants. Prompt 2: “Based on the city above, describe the ruling plant queen.” Prompt 3: “Write a speech she gives during the annual leaf festival.” Each prompt refines and deepens the narrative.
Defines a personality or entity in a prompt, especially useful in storytelling or games. Example: Prompt: “A cyber-shaman who guides lost travelers through neon dreams, wearing feathers and fiber optics.” Instantly visual. Immediately story-rich.
Prompt tags that manipulate brightness and shadow for more dramatic visuals. Example: Prompt: “Portrait of a swordswoman, high contrast, dramatic lighting, black background.” “High contrast” pulls light and shadow into play, intensifying mood.
The maximum length of input text an AI model can process at one time. Example: Prompt: ‘summarize this 10-page article.” Only parts may be used if the context window is limited — manage prompt size wisely.
A numeric value to anchor randomness in generative AI, ensuring repeatable results. Example: Prompt: “Fantasy forest, 16:9, 8k, seed=34592” You can regenerate the “exact same” image using this seed.
A generative model used for creating high-quality images from text prompts. Example: Prompt: “A sci-fi corridor made of liquid glass, soft shadows.” The AI “diffuses” noise into clarity – each step refines the dream.
Used in image generation to define spatial depth and enhance realism. Example: Prompt: “Underwater temple, visible depth map, cinematic lighting.” Adds dimension. Feels like you could dive into it.
Using logic, data, or variables to change prompt elements in real-time. Example: Prompt: “Generate a headline based on today’s top news article.” Your input changes daily. The prompt adapts with it.
A numeric representation of words or images used by models to understand relationships. Example: Prompt: Cluster AI embeddings for terms: love,anger, joy, fear. Shows how emotionally distant or close concepts are.
A measurement of randomness in output—low entropy is focused, high entropy is diverse. Example: Prompt: “Generate 5 logo ideas for an AI startup, entropy=high.” You’ll get wild, unexpected, creative variations.
A prompt designed to test creative boundaries or model limits. Example: Prompt: “Describe what silence looks like in the style of ancient architecture.” Impossible? Or poetic breakthrough?
Training a pre-existing AI model on a specific dataset to customize its behavior. Example: A company fine-tunes a language model with its internal documents to create a custom AI assistant trained in corporate language and tone.
Providing a few examples within the prompt to guide the model’s output. Example: Prompt: ‘translate these sentences into French: 1. Hello = Bonjour 2. Good night = Bonne nuit 3. Thank you =…” The AI will continue in pattern, completing the translation based on few-shot context.
Used in image prompts to focus on the main subject with clarity and contrast. Example: Prompt: “A golden retriever in sharp focus in the foreground, blurred background, sunny park scene.” “Foreground emphasis” ensures the dog is the focal point.
Any AI that produces content, including text, images, or audio from a prompt. Example: Tools like ChatGPT, Midjourney, and DALL·E are forms of generative AI – transforming inputs into content that didn’t previously exist.
In diffusion models, controls how closely the output follows the prompt. Example: Prompt: “A glowing forest, magical atmosphere, guidance scale: 8.0” Higher values result in more literal interpretation of the prompt.
A learning algorithm used to adjust the model’s weights during training. Example: Not used directly in prompts, but fundamental to how models learn by gradually minimizing prediction errors.
Settings that control the learning process of AI, such as learning rate or batch size. Example: Prompt designers may not adjust hyperparameters directly—but understanding them helps interpret model behavior and performance.
In upscaling workflows, corrects artifacts and boosts image clarity after initial generation. Example: Midjourney’s “upscale (2x)” function applies high-res fix to clean edges, refine textures, and eliminate low-res artifacts.
A prompt that uses a base image and applies transformations or enhancements. Example: Start with a pencil sketch and use a prompt like “convert to digital painting, glowing sunset background, cinematic detail”. The AI reshapes the image based on your instruction.
Filling in or altering parts of an image using AI, often guided by a prompt. Example: Upload an image with a missing corner, then prompt: “replace missing section with shattered glass effect in cyberpunk style”. AI “heals” the gap using imagination + instruction.
Training AI models specifically to follow detailed human instructions more reliably. Example: Modern ChatGPT models have been instruction-tuned to follow prompts like: ‘summarize this as a tweet in under 280 characters.”
Using structured data (like JSON) as input or output format in system-driven prompts. Example: Prompt: “Generate a product catalog in JSON format with item name, price, and description.” Useful for developers working with AI via API.
Using multiple descriptors in one prompt to control output style or context. Example: “Vintage, cinematic, wide angle, dusk, 35mm film, grainy texture, lens flare” – all stacked to steer aesthetic.
An abstract representation space where AI generates and manipulates concepts before rendering. Example: Think of “cyberpunk café” not as literal words, but as coordinates in latent space the AI pulls from.
A neural network trained to predict and generate human-like text. Example: When ChatGPT writes a poem in your voice, it’s the language model at work – predicting your next poetic breath.
A technique in deep learning to stabilize and accelerate training. Example: Not directly promptable, but vital behind-the-scenes for models that don’t drift into chaotic outputs.
A popular AI image generator known for stylized, artistic outputs. Example: Prompt: ‘surrealist dreamscape of a floating forest at twilight, high contrast, Midjourney style.” The engine interprets the abstract with cinematic elegance. Here you can directly access to Midjourney
The structural layout of an AI model, including layers, attention, and more. Example: While not part of prompt writing, knowing the architecture (like GPT vs. diffusion) helps shape expectations of the result.
Used in image or text models to isolate regions or elements for focused modification. Example: “Inpaint the left half of this portrait to show aged version of the same person.” The prompt targets only part of the image — the rest stays untouched.
A command telling the AI what to avoid—crucial for removing unwanted features. Example: Prompt: “Portrait of a woman, realistic, soft lighting, -no glasses, -no watermark, -no distortions.” Negative tags steer the output away from unwanted artifacts.
A machine learning system inspired by the structure of the human brain. Example: You don’t prompt a neural net directly, but you use it every time a model like ChatGPT processes your prompt.
Controls how noise is introduced and removed in image diffusion models. Example: Higher noise at start = abstract. Controlled noise = smoother realism. It’s the chaos the art emerges from.
When a model becomes too tailored to training data and performs poorly on new inputs. Example: A fine-tuned model trained only on legal text may fail to answer simple creative prompts — it’s too specialized.
The words or segments generated by a language model in response to a prompt. Example: Prompt: “Write a haiku about electricity.” The response might be 17 syllables = ~10 output tokens.
The discipline of crafting effective prompts to direct AI outputs. Example: A prompt engineer might test 10 variations to generate the best image caption: tone, framing, audience all considered.
Adjusting model settings or weights to optimize results. Example: In open-source models, adjusting learning rate or image resolution can change output quality drastically.
A metric for how confidently a language model predicts the next word. Example: Lower perplexity = more predictable text (great for factual writing). Higher = more creative output. Here you can directly access to Perplexity
A method to link multiple prompts into a flowing interaction or narrative. Example: Step 1: “Describe a futuristic world.” Step 2: “Now write a love letter set in that world.” Step 3: “Create a recipe from that world.” You’re building a fictional universe through chained prompts.
A structured format for interacting with APIs or search systems using prompts. Example: “search?q=AI+generated+art&sort=latest” Query strings form the bones of how data gets requested and filtered online.
A learning method where models learn from reward-based feedback. Example: ChatGPT’s ability to respond helpfully improved through reinforcement learning from human feedback (RLHF).
Increasing image resolution post-generation for sharper outputs. Example: Prompt: “Generate a 512x512 fantasy landscape, then upscale to 2048x2048 with enhanced textures.” You’re scaling resolution without losing detail.
A number that determines the randomness in AI generation—used for reproducibility. Example: Prompt: “Cyberpunk samurai, sunset, seed=84712” This ensures consistent results when re-used.
Using prompts to apply the style of one image or artist to another visual input. Example: Prompt: “Apply Van Gogh’s brush style to a modern photo of a city skyline.” The city inherits the emotion of a painter’s world.
Algorithm controlling how outputs are selected from probability distributions. Example: Changing from “greedy sampling” to “top-k sampling” can make your prompt results more creative and less repetitive.
Breaking input into chunks (tokens) that an AI model can process. Example: The word “sunlight” becomes one token. The phrase “a beam of golden sunlight” becomes several. Token limits define how much the model can hold at once.
A setting that controls creativity in text generation—lower = focused, higher = wild. Example: Prompt: “Write an ad slogan for a toothbrush. Temperature: 0.2” → “Clean teeth. Guaranteed.” Now with temperature: 0.9 → “Blast off into freshness!”
The dataset used to teach an AI model how to generate or classify content. Example: A model trained on academic text will sound formal. A model trained on Reddit will sound… spicy.
Enhancing resolution or size of AI-generated images without losing quality. Example: Prompt: “Generate a fantasy castle, 1024x1024, upscale 4x.” The output is enlarged with better textures, not blurry pixels.
A feedback system where human input continuously improves model output. Example: When you thumbs-up or edit an AI response, that data helps retrain future versions. You become part of the model’s learning system.
Represents data as points in a high-dimensional space for model understanding. Example: Words like “king” and “queen” are close in vector space. That’s why the AI links them in logic and meaning.
AI-based reproduction of real or synthetic human voices. Example: Prompt: “Read this script in the voice of Morgan Freeman.” The output mimics pacing, tone, rhythm — even intonation.
The importance assigned to features or words in a model during training or inference. Example: Prompt: “A forest with a glowing waterfall::2 and tiny fireflies::1” “::2” gives the waterfall twice the visual influence.
A subtle, emotionally toned prompt that nudges creativity rather than commanding it. Example: Prompt: “A memory of a goodbye in the rain, in muted colors and gentle blur.” Soft instruction, powerful emotion — that’s a true whisper.
A visual prompt directive used to anchor objects horizontally in an image. Example: Prompt: “A line of vintage cars along the x-axis, left to right, sunset backdrop.” Guides composition across horizontal space.
A logic used to stop or continue AI output based on custom criteria. Example: Prompt: “summarize until 3 key points are covered. Then stop.” AI knows when to finish — no wandering, no fluff.
Guiding AI to create images with specific perspective depth or magnification. Example: Prompt: “Zoom into the eye of a storm, ultra-wide lens, swirling detail.” You’re not just showing a scene — you’re stepping inside it.