Safeguarding LLM Outputs: Testing, Evaluation, and AI Safety

As generative AI becomes integrated into modern applications, ensuring that large language models produce safe, accurate, and unbiased results is a critical challenge. This text-only course guides you from foundational AI safety concepts to practical evaluation strategies. You will understand how to systematically test model responses, mitigate risks like prompt injection, and implement automated guardrails. What you'll learn: - Understand the core principles of LLM safety, alignment, and common failure modes. - Learn how to design and execute red-teaming strategies to uncover model vulnerabilities. - Apply modern evaluation metrics to assess accuracy, bias, and toxic outputs. - Configure real-time guardrails to filter and sanitize both inputs and outputs. - Evaluate the safety and relevance of retrieval-augmented generation (RAG) systems. - Practice setting up automated testing pipelines to continuously monitor model behavior. The course begins with essential terminology and safety frameworks before walking you through step-by-step methodologies for manual and automated testing. This course is designed for software developers, product managers, and AI enthusiasts who want to build secure AI systems, with no prior experience in AI safety required. Start reading today to master the essential techniques for building trustworthy language model applications.