myAIdojo Belt Certification Requirements
Our certification system provides a rigorous assessment of AI skills with industry-relevant challenges. Each belt represents a significant milestone in your AI mastery journey. Below you'll find detailed requirements for each certification level.
White Belt Certification
Focus Areas
- Fundamentals of AI interaction and basic concepts
- Understanding various AI models and their capabilities
- Basic prompt engineering techniques
- Simple workflow construction
- Ethical considerations in AI usage
Prerequisites
No prior experience required. This certification is designed for beginners who want to validate their basic understanding of AI concepts and interaction patterns.
Assessment Format
Total Duration: 90 minutes
- Knowledge Assessment (30 min): Multiple-choice questions covering AI fundamentals, capabilities, and ethics
- Practical Prompt Engineering (30 min): Create effective prompts for specific outcomes across different AI models
- Simple Workflow Creation (30 min): Build a basic AI workflow that combines two models to solve a simple problem
Passing Criteria
- Minimum 70% score on knowledge assessment
- Successful completion of at least 4 out of 6 prompt engineering challenges
- Functional workflow that meets basic requirements
Sample Assessment Questions
Which of the following best describes the difference between a generative AI model and a discriminative AI model?
Create a prompt that would instruct an AI to generate a 5-day itinerary for Paris that includes at least one museum, one historical site, and one culinary experience each day.
Build a simple workflow that takes user input, uses a language model to identify key themes, and then uses an image generation model to create a related visual.
Yellow Belt Certification
Focus Areas
- Applied AI skills across multiple domains
- Intermediate prompt engineering across different AI models
- Basic workflow composition with feedback loops
- Content creation with AI assistance
- Problem-solving with AI tools
- Data handling fundamentals
Prerequisites
White Belt certification or equivalent experience. Familiarity with multiple AI models and basic prompt engineering techniques is required.
Assessment Format
Total Duration: 2 hours
- Advanced Prompt Engineering (45 min): Create specialized prompts for specific outcomes with constraints across different AI models
- Multi-Model Integration (45 min): Build workflows that combine multiple AI models with proper data handling
- Problem-Solving Scenarios (30 min): Apply AI tools to solve realistic problems in business, creative, or technical domains
Passing Criteria
- Successful completion of at least 5 out of 8 advanced prompt engineering challenges
- Functional multi-model workflow that correctly handles data between models
- Effective solutions to at least 2 out of 3 problem-solving scenarios
Sample Assessment Questions
Create a prompt chain that first extracts key information from a product description, then generates marketing copy targeting three different audience segments, ensuring consistent product details across all outputs.
Build a workflow that takes a user's text description, generates an outline using a language model, creates an image based on that outline, and then generates a caption for the image that aligns with the original intent.
A small business owner needs to analyze customer feedback from various sources (social media, email, and surveys). Create an AI workflow that categorizes feedback, identifies common themes, and generates actionable insights.
Green Belt Certification
Focus Areas
- Complex workflow creation for specialized applications
- Advanced prompt engineering techniques
- Domain-specific AI implementations
- Error handling and edge cases
- Performance optimization
- Adaptability across different AI models
Prerequisites
Yellow Belt certification plus at least 3 months of documented practice. Candidates should have experience building AI workflows and solving domain-specific problems.
Assessment Format
Total Duration: 3 hours
- Complex Workflow Implementation (90 min): Build sophisticated AI workflows that handle errors, edge cases, and optimize for performance
- Domain Specialization Project (60 min): Implement an AI solution for a specific domain (choose from creative content, data analysis, business automation, or 3D creation)
- Technical Interview (30 min): Defend your implementation decisions and demonstrate understanding of advanced concepts with a Sensai
Passing Criteria
- Fully functional complex workflow that properly handles errors and edge cases
- Domain project that meets all specified requirements and demonstrates domain expertise
- Successful defense of implementation decisions in technical interview
Sample Assessment Challenges
Create a content moderation workflow that processes text input through multiple AI models, handles edge cases like ambiguous content, implements retry logic for API failures, and provides explanations for moderation decisions.
Build an AI-assisted scriptwriting system that generates scene descriptions, character dialogue, and storytelling elements based on user input, while maintaining narrative consistency and character voice throughout.
Explain your approach to optimization in your workflow. What specific techniques did you implement to improve performance, reduce token usage, and enhance output quality?
Blue Belt Certification
Focus Areas
- Professional-grade AI implementation and optimization
- System integration with external APIs and services
- Performance tuning and cost efficiency
- Sophisticated error handling and recovery
- Advanced domain specialization
- Mentoring and knowledge transfer
Prerequisites
Green Belt certification plus at least 6 months of documented practice. Candidates should have experience implementing complex AI solutions and optimizing performance in real-world scenarios.
Assessment Format
Total Duration: 4 hours on-site + 1 week take-home project
- System Design (60 min): Design a professional-grade AI system architecture that meets specific business requirements
- Performance Optimization (90 min): Analyze and optimize an existing AI workflow for cost, speed, and reliability
- Integration Challenge (90 min): Implement external API integrations and data transformation pipelines
- Take-Home Project (1 week): Develop a complete AI solution for a complex problem, including documentation, testing, and deployment considerations
Passing Criteria
- System design that demonstrates professional architectural patterns
- Measurable performance improvements with documented optimizations
- Successful external integrations with proper error handling
- Fully functional take-home project that meets all requirements
- Comprehensive documentation and testing
Sample Assessment Challenges
Design an AI-powered content moderation system for a social media platform that needs to process 1,000 posts per minute, handle images and text, maintain 99.9% uptime, and provide detailed violation reports to administrators.
This AI workflow currently costs $0.85 per run and takes an average of 12 seconds to complete. Optimize it to reduce cost by at least 40% and execution time by at least 30% while maintaining output quality.
Develop an AI-powered customer support triage system that analyzes incoming support tickets, classifies them by urgency and department, extracts key information, generates initial responses, and provides analytics on support volume and resolution times.
Brown Belt Certification
Focus Areas
- End-to-end AI solution architecture at scale
- Enterprise-grade workflow implementation
- Advanced system integration patterns
- Deployment and scaling strategies
- Security and compliance considerations
- Cost management for production systems
- Performance under high demand
Prerequisites
Blue Belt certification plus at least 1 year of documented practice. Candidates should have experience deploying and managing AI solutions in production environments and working with enterprise systems.
Assessment Format
Total Duration: 8-hour challenge + 2-week capstone project
- Enterprise Architecture (2 hours): Design a scalable, enterprise-grade AI architecture that addresses specific business requirements
- Security & Compliance Review (2 hours): Analyze and address security vulnerabilities and compliance requirements in AI systems
- Scaling Challenge (2 hours): Design and implement strategies to scale an AI solution from hundreds to millions of users
- System Integration (2 hours): Connect AI workflows with enterprise systems using appropriate patterns
- Capstone Project (2 weeks): Develop a complete, production-ready AI solution for a complex enterprise problem
Passing Criteria
- Enterprise architecture that demonstrates scalability, reliability, and maintainability
- Comprehensive security and compliance plan
- Effective scaling strategies with performance metrics
- Successful enterprise integrations using appropriate patterns
- Production-ready capstone with deployment documentation, monitoring, and maintenance plans
Sample Assessment Challenges
Design an AI-powered decision support system for a financial institution that needs to process customer data from multiple legacy systems, comply with financial regulations, maintain audit trails, and deliver real-time recommendations to advisors.
This AI system processes healthcare data to assist with diagnosis. Identify all potential security vulnerabilities and compliance issues, then develop a comprehensive plan to address them while maintaining system performance.
Develop an enterprise-grade AI platform that enables non-technical business users to create, deploy, and monitor AI workflows for their departments. The solution must integrate with existing enterprise systems, scale to support thousands of users, ensure proper governance, and provide detailed analytics.
Black Belt Certification
Focus Areas
- Mastery and innovation in AI implementation
- Novel AI workflow patterns and architectures
- Advanced teaching and mentoring capabilities
- AI ethics leadership and governance
- Industry-specific expertise and best practices
- Contributions to the AI community
- Strategic integration of AI into organizational objectives
Prerequisites
Brown Belt certification plus at least 18 months of documented practice. Candidates must submit a portfolio of completed AI projects demonstrating excellence across multiple domains and showing evidence of innovation or leadership in the field.
Assessment Format
Total Duration: 2-day comprehensive assessment + research project
- Innovation Challenge (4 hours): Develop a novel approach to an existing AI problem that demonstrates significant improvements over current methods
- Technical Mastery (4 hours): Demonstrate mastery across multiple AI domains through a series of advanced challenges
- Knowledge Transfer (2 hours): Create educational materials and teach a complex AI concept to junior practitioners
- Ethics & Governance (2 hours): Develop comprehensive ethical guidelines and governance frameworks for AI implementations
- Research Project (4-8 weeks): Conduct original research or develop an innovative AI solution that advances the state of the art
Passing Criteria
- Demonstrated innovation that provides measurable improvements over existing approaches
- Technical mastery across all required domains
- Effective knowledge transfer as evaluated by students and peers
- Comprehensive ethics and governance framework that addresses complex scenarios
- Research project of publication quality that contributes new knowledge or techniques to the field
Sample Assessment Challenges
Current AI-assisted coding systems struggle with large-scale refactoring tasks. Develop a novel approach that improves the accuracy and reliability of AI-assisted code refactoring by at least 30% compared to existing methods.
Create a comprehensive teaching module on "Advanced prompt engineering for zero-shot generalization" that would enable intermediate practitioners to understand and apply these techniques effectively. Include hands-on exercises, assessment methods, and common pitfall guidance.
Conduct original research on a topic of your choice that advances the state of AI implementation. Your project should address a significant challenge in AI application, provide empirical evidence of improvement, and contribute new patterns or techniques that others can adopt. The project must be of publication quality and include comprehensive documentation.