(generative Ai Leader) Techniques To Improve Genai Model Output
 

Generative AI models require structure and oversight to deliver reliable business results. This course will teach you to design, optimize, and govern GenAI systems for accurate, grounded, and enterprise-ready output.
What you'll learn
Generative AI models can produce impressive results, but without the right strategies, they can also introduce hallucinations, bias, and inconsistent outputs. In this course, (Generative AI Leader) Techniques to Improve GenAI Model Output, you'll learn how to improve the reliability, accuracy, and business value of generative AI systems using Google Cloud best practices.
First, you'll explore how to address common model limitations using techniques like grounding, retrieval-augmented generation (RAG), prompt engineering, fine-tuning, and human-in-the-loop workflows, along with monitoring strategies such as KPI tracking and drift detection. Next, you'll discover how prompt engineering techniques-from zero-shot to advanced methods like chain-of-thought-can improve reasoning and output consistency. Finally, you'll learn how grounding and model configuration settings enhance trust, control, and accuracy.
When you're finished with this course, you'll have the skills and knowledge of generative AI optimization needed to design systems that produce accurate, grounded, and enterprise-ready results and prepare for the Google Cloud Certified Generative AI Leader certification.