Introduction to Testing AI Models, LLMs and Chatbots

Gain basic understanding how to test foundation models, LLMs, chatbots and AI bases systems.


What you'll learn
Understand how AI is working
Understand basic software testing
Understand how AI is tested compared to traditional software
Gain knowledge on testing for ethics
Understand how to test reasoning abilities of AI
Understand functional AI testing
Gain know how on how to validate NPL
See how to benchmark AI against models "HellaSWAG, MMLU, CODEXGLUE, BLEU, Humaneval"
Importance of test data and model drifting
See how chatbots can be tested with real chatgpt examples

Requirements
basic experience with software testing
basic coding experience ( but not needed)
optional - GPT model 4 subscription (but not needed)
desire to learn the hottest skill on the market
curiosity and 5-6 hours to finish the material

Description
Welcome to "Testing AI: Foundation Models, LLMs, Chatbots & More," your comprehensive guide to understanding the fundamentals of testing advanced AI systems. Whether you're a developer, a data scientist, or simply an AI enthusiast, this course will equip you with the knowledge and skills necessary to assess and improve the reliability, performance, and safety of AI technologies.
What You Will Learn:
Introduction to AI Testing:
Understand the importance of testing AI systems, including ethical considerations and the potential impacts of AI failures.
Testing Basics:
Learn about different types of testing methodologies like unit testing, integration testing, and system testing as applied to AI.
Special Focus on Foundation Models and LLMs:
Dive deep into the challenges and techniques for testing large language models and foundational AI systems that are reshaping numerous industries.
Chatbot Testing:
Explore the unique aspects of testing conversational AI, ensuring they respond accurately and appropriately in varied scenarios.
AI System Evaluations:
Learn how to design and implement effective testing regimes for different AI-based systems, using both manual and automated tools.
Performance Metrics:
Understand the key performance indicators (KPIs) and metrics essential for evaluating AI system efficiency and effectiveness.
Case Studies:
Gain insights from real-world scenarios that highlight common pitfalls and best practices in AI testing.
Ethical AI
: understand the risk with AI and the ethics behind AI. How can and should you test for this
Benchmarking
: Understand how to test the AI against some common benchmarking models such as: BLUE, HellaSWAG, MMLU, CODEXGLUE, HumanEval
Who This Course Is For:
This course is ideal for anyone looking to gain a solid grounding in the techniques and practices essential for testing AI systems. Whether you’re starting a career in AI, looking to enhance your professional skills, or interested in the mechanisms behind AI system reliability, this course has valuable insights for you.
Course Features:
Engaging video lectures
Practical assignments and hands-on projects
Quizzes and exams to test your knowledge
Access to a community forum for discussion and collaboration
Lifetime access to course materials
Enroll now to start mastering the crucial skill of testing AI systems and ensure you’re prepared to contribute to the development of safe and reliable AI technologies!
Who this course is for:
Citizen Developer
Software testers
quality engineers

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