Overview
Discover prompt engineering techniques for quality engineers to effectively test AI models and LLMs, ensuring robust, fair, and transparent AI applications through systematic testing approaches.
Syllabus
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- Introduction to Prompt Engineering for AI
-- Overview of AI and LLMs
-- Importance of prompt engineering in AI testing
- Understanding AI Model Testing
-- Basics of AI model functionality
-- Common testing strategies for AI models
-- Key challenges in AI testing
- Fundamentals of Prompt Engineering
-- Defining prompts for AI testing
-- Types of prompts and their applications
-- Best practices in prompt development
- Systematic Testing Approaches
-- Designing robust test cases for AI models
-- Techniques for testing LLMs
-- Ensuring test coverage and thoroughness
- Ensuring Fairness in AI Models
-- Identifying and mitigating bias in AI
-- Testing for ethical and unbiased outcomes
-- Case studies: Prompt engineering for fairness
- Ensuring Transparency in AI Systems
-- Techniques for explaining AI behaviors via prompts
-- Building interpretable and transparent testing frameworks
-- Analyzing and reporting prompt test outcomes
- Advanced Prompt Engineering Techniques
-- Iterative prompt refinement methods
-- Automation in prompt generation and testing
-- Real-world applications and industry tools
- Practical Workshop: Applying Prompt Engineering
-- Hands-on exercises with real AI models
-- Collaborative design of prompt-based test scenarios
-- Peer review and analysis of testing results
- Conclusion and Future Directions
-- The evolving role of prompt engineering in AI
-- Emerging trends and technologies
-- Resources for continuing education and skill development
- Assessment and Evaluation
-- Final project: Design and execute a prompt testing plan
-- Reflection and feedback session
Taught by
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On-Demand
