GPT Image 2 for Scientific Figures: Prompt Design & Editable Workflow Guide

Introduction

Stop simply copying your paper abstract into AI image generators — that is where most people go wrong when creating scientific figures with AI. For researchers, a scientific figure is not merely an illustration. It is an essential part of the storytelling process of a paper.

A well-designed figure performs a form of information compression: within a limited visual space, it helps readers quickly understand: what problem the study addresses; what methodology is proposed; where the key innovation lies; why the proposed approach works.

The core workflow should be: Paper Understanding → Figure Type Selection → Information Abstraction → Visual Encoding

Figure 1: AI-Powered Scientific Figure Generation Pipeline

Our Approach: Making AI Understand Research Logic

A better approach is to explicitly instruct the model to analyze: the research problem; the technical pipeline; the core contributions; the experimental validation logic; the relationship between different components. The process: Paper Content → Research Logic Analysis → Visual Design → Scientific Figure Generation

Scientific Figure Types Should Match Research Paradigms

Research Paradigm	Figure Type	Visual Focus
Theoretical proposal	Conceptual framework	New concepts, hypotheses
Algorithm design	Method overview, pipeline	Problem, solution strategy
Deep learning architecture	Network architecture	Components, info flow
Component improvement	Module zoom-in, comparison	Structural changes
Reasoning mechanism	Information flow, causal graph	Internal processing
Dataset engineering	Data lifecycle pipeline	Collection, processing
Benchmark/evaluation	Evaluation framework	Tasks, metrics, protocols
LLMs/multimodal	Multimodal framework	Data fusion, knowledge
Agents/embodied AI	Closed-loop workflow	Perception, action, feedback
Cross-disciplinary AI	Domain-AI framework	Domain knowledge, validation

Figure 2: Research Paradigm to Figure Type Mapping

Visual Style: Nature, Science, IEEE Standards

Clean 2D vector style; White background; Minimal noise; Limited color palette; Colors only for functional grouping. Output should resemble Method Overview figures, Framework diagrams — not PowerPoint slides or marketing graphics.

Visual Hierarchy Matters

Highest priority: Core innovation, proposed method. Second: Overall workflow, data relationships. Supporting: Input data, experiments, results.

Complete Prompt Template

You are a professional scientific visualization designer with expertise in CS, AI, engineering systems, and data science. Design publication-quality figures following Nature/Science/IEEE standards.

Analyze: research background; scientific question; research paradigm; technical methodology; data flow; experimental validation; main contributions.

The figure should communicate: What problem is addressed; What method is proposed; Why the method works; What value it provides.

Select figure structure based on research type. Apply strict visual hierarchy. Use clean 2D vector style, white background, limited palette. Colors only for functional grouping.

Making AI Figures Editable (SVG Workflow)

Simply ask: "Convert this scientific figure into SVG code. Keep every module, label, arrow, and icon as an independently editable element." Then refine in Adobe Illustrator, Figma, or Inkscape.