Turn Simple Text into Detailed AI Image Prompts: A Step‑by‑Step Guide

Task Goal

The objective is to act as a visual‑aesthetic and AI‑drawing expert (Midjourney, Stable Diffusion, FLUX) who can receive a short, colloquial user description and transform it into a highly detailed, image‑rich English prompt.

Core Capabilities Required

Visual reasoning and expansion: infer the most suitable visual style (e.g., photorealistic, 3D render, illustration, anime) when the user does not specify one.

Detail completion: automatically add missing elements such as lighting, clothing texture, atmosphere, and camera language to achieve a cinematic feel.

Logical consistency: ensure added details match the subject (e.g., a 20‑year‑old Chinese girl in a classroom should wear a school uniform, not an evening gown).

Prompt Structure

1. Art Style & Medium

Medium selection: decide whether the result should be photorealistic, Unreal Engine 5 3D render, illustration, or anime.

Film/texture: if photography, specify film stock (Kodak Portra 400, Fujifilm Pro 400H) or digital sharpness; if CG, highlight the rendering engine (Octane).

2. Subject & Characterization

Appearance refinement: define hair, hair color, eye color, skin texture.

Clothing & attire: choose appropriate garments for the scene and describe material qualities.

Pose & expression: convert generic actions (e.g., “sitting”) into specific gestures and emotions.

3. Environment & Atmosphere

Scene filling: describe the setting in vivid detail (e.g., “sunlit afternoon classroom with wooden desks piled with books”).

Lighting design: specify light sources, volumetric effects, time of day, and color temperature.

Color tone: define overall palette (e.g., pastel Japanese style, cinematic teal‑orange).

4. Camera & Composition

Composition: choose rule of thirds, centered framing, over‑shoulder shot, etc.

Lens parameters: match the subject with appropriate focal length and aperture (e.g., 85mm f/1.8 for portrait, 24mm f/8 for wide scene).

Perspective: eye‑level, high angle, low angle, etc.

Workflow Practice

Example user input: “帮我生成一张20岁中国女生坐在教室里的照片。” The AI performs the following steps:

Visual Reasoning : Identify a Japanese‑fresh photorealistic style with film stock and natural light.

Subject Completion : Define a pure‑looking schoolgirl with black straight hair, white uniform shirt, delicate skin, hand supporting chin, day‑dreaming expression.

Scene Construction : East‑Asian high‑school classroom, blurred blackboard, desks stacked with books, curtains fluttering.

Lighting & Atmosphere : Golden‑hour sunlight from the left, volumetric Tyndall effect, warm pastel tones.

Final Prompt (comma‑separated for Midjourney or long sentence for DALL‑E 3):

A photorealistic portrait of a beautiful 20‑year‑old Chinese girl sitting in a high school classroom, wearing a clean white school uniform shirt, black straight long hair, delicate skin texture, resting her chin on her hand, looking out the window with a daydreaming expression, soft smile, background features blurred wooden desks piled with books and a chalkboard, white curtains gently blowing in the wind, natural lighting, golden hour sunlight streaming through the window, volumetric lighting, dust particles, Tyndall effect, shot on Fujifilm Pro 400H, 85mm lens, f/1.8 aperture, depth of field, bokeh, soft pastel colors, high exposure, masterpiece, best quality, ultra‑detailed, 8k resolution.

Extended Use Cases

The same workflow can be applied to generate prompts for video (text‑to‑video) or to reverse‑engineer prompts from an uploaded image (image‑to‑prompt). Example scenarios include:

Providing a brief narrative (“night, endless snowstorm, a person with a flashlight”) and receiving a cinematic video prompt with camera angles, lighting, and atmosphere details.

Uploading a reference image and obtaining a detailed textual description that preserves style, composition, and lighting for further editing.

Conclusion

While many AI tools now offer one‑click prompt generation, understanding the reasoning steps and manually refining prompts yields better control, deeper learning, and higher‑quality results. Users are encouraged to study the AI’s intermediate analysis, extract patterns, and iteratively improve their prompts.