A specialized system prompt collection designed to create conversational chatbots for ideating AI assistant and agent concepts. This repository contains multiple prompt variants that transform a general-purpose LLM into a focused brainstorming partner for AI solution ideation.
These system prompts guide an AI to generate creative, practical ideas for AI assistants and agents based on user requirements. They're particularly useful for:
- Brainstorming new AI assistant concepts
- Exploring potential AI solutions for specific domains
- Generating batches of AI agent ideas with varying capabilities
- Discovering innovative applications for AI technology
- Developing detailed concepts through collaborative ideation
This repository includes multiple variants of the AI Assistant Idea Generator, each with a unique approach to the ideation process:
- Original: The classic approach with randomized ideation workflows
- Domain Expert: Focuses on creating assistants tailored to specific fields or industries
- Problem Solver: Takes a methodical approach focused on identifying pain points first
- Future Technology: Explores forward-looking assistants that anticipate emerging technologies
- Collaborative Ideation: Emphasizes iterative refinement and co-creation of assistant concepts
- User Persona: Focuses on understanding end-user needs, behaviors, and contexts
For detailed information about each variant and when to use them, see Prompt Variants Documentation.
.
├── README.md # This file
├── avatar.webp # Project avatar image
├── system-prompts/ # System prompt files
│ ├── v1.md # Original system prompt
│ ├── templates/ # Base templates for creating new variants
│ └── variants/ # Different variants of the system prompt
│ ├── original.md # Copy of the original prompt
│ ├── domain_expert.md # Domain-specific approach
│ ├── problem_solver.md # Problem-first approach
│ ├── future_tech.md # Forward-looking approach
│ ├── collaborative_ideation.md # Interactive co-creation approach
│ └── user_persona.md # User-centered design approach
├── examples/ # Example conversations using each variant
├── docs/ # Documentation
│ ├── prompt_variants.md # Detailed explanation of each variant
│ └── integration_guide.md # Guide for integrating with different frameworks
While the system prompts work excellently in conversational interfaces, they can also be adapted for programmatic use with LangChain and prompt templating. Here's a basic example:
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from langchain.chains import LLMChain
# Load the system prompt variant of your choice
with open("system-prompts/variants/domain_expert.md", "r") as file:
system_prompt = file.read()
# Create a template with variables
template = f"""
{system_prompt}
DOMAIN: {{domain}}
EXPERTISE_LEVEL: {{expertise_level}}
NUMBER_OF_SUGGESTIONS: {{num_suggestions}}
"""
prompt = PromptTemplate(
input_variables=["domain", "expertise_level", "num_suggestions"],
template=template
)
# Initialize the LLM and chain
llm = OpenAI(temperature=0.8) # Higher temperature for creativity
chain = LLMChain(llm=llm, prompt=prompt)
# Generate ideas
response = chain.run(domain="healthcare", expertise_level="professional", num_suggestions=5)
print(response)For more integration examples with different frameworks and platforms, see the Integration Guide.
The /examples directory contains sample conversations for each variant:
- Domain Expert Example: Healthcare AI assistants for clinical settings
- Problem Solver Example: Addressing remote work challenges
- Future Technology Example: Next-generation education assistants
- Collaborative Ideation Example: Developing a personal finance assistant
- User Persona Example: Assistants for creative professionals
Feel free to fork this repository and adapt the system prompts for your specific needs. If you develop interesting variations or improvements, pull requests are welcome!
This project is open source and available for use and modification.
Last updated: March 26th, 2025