While most GenAI applications focus on text, image, or code generation, one powerful but underutilized application is generating structured diagrams like flowcharts or state machines directly from natural language.
Imagine describing a process in plain English and instantly getting a visual flowchart.
This has huge implications for:
- Business Analysts creating process docs
- Developers documenting APIs or microservices
- Educators converting concepts into visuals
We’ll use an LLM (like GPT-4 or Claude) to:
- Extract structure from user input
- Convert that into a Graphviz DOT or Mermaid.js diagram
- Render the flowchart in HTML
Tools Used
- OpenAI or Groq GPT-4 / Claude / Mixtral
- Python (for prompt + rendering)
- graphviz or mermaid for diagrams
- Streamlit (optional for UI)
Example Prompt
“Create a flowchart for user signup process. Steps:
User submits form → Backend validates input → Save to DB → Send welcome email → Done.”
Step 1: Use LLM to Generate DOT Format
import openai
SYSTEM_PROMPT = """You are an expert flowchart generator. Convert plain text process descriptions into DOT format.
Use "->" for direction and include shape info like [shape=box] for each step."""
user_input = "User submits form → Backend validates input → Save to DB → Send welcome email → Done."
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": user_input}
]
)
dot_code = response['choices'][0]['message']['content']
print(dot_code)
Expected Output (DOT format):
Process Flowchart
Step 2: Render the Flowchart
# First, install Graphviz in your terminal
# sudo apt install graphviz
from graphviz import Source
# Define DOT code
dot_code = """
digraph G {
"User submits form" [shape=box];
"Backend validates input" [shape=diamond];
"Save to DB" [shape=box];
"Send welcome email" [shape=box];
"Done" [shape=ellipse];
"User submits form" -> "Backend validates input";
"Backend validates input" -> "Save to DB";
"Save to DB" -> "Send welcome email";
"Send welcome email" -> "Done";
}
"""
# Render to PNG and open automatically
src = Source(dot_code)
src.render("signup_flowchart", format="png", view=True)
Render in Web Using Mermaid
graph TD
A[User submits form] --> B[Backend validates input]
B --> C[Save to DB]
C --> D[Send welcome email]
D --> E((Done))
You can even get the LLM to output Mermaid syntax instead of DOT.
Real-World Use Cases
- Low-Code Workflow Builders
- Process Automation Tools (e.g., Zapier clone)
- Software Design Docs
- Chatbot Flow Generators
Advantages:
Feature | Benefit |
---|---|
No GUI Needed | Just type the steps |
Model-Agnostic | Works with any LLM with CoT ability |
Extensible | Add branches, conditions, loops |
Visual + Verbal Output | Explain and visualize in one go |
Challenges
- Handling conditional logic
- Converting vague inputs to strict structure
- Supporting complex branches or loops
You can solve these using prompt chaining or function calling with validation.
Summary
Prompt-to-Flowchart is an amazing blend of structured reasoning and visual generation. With just one natural language prompt, you can create:
- Diagrams
- Workflows
- Design Docs
- System Blueprints
Planning to develop an AI software application? We’d be delighted to assist. Connect with Jellyfish Technologies to explore tailored, innovative solutions.