Google Cloud Generative AI Leader: Section 3: Techniques to improve gen AI model output

The provider lists this domain at 20% of the exam.

In plain English

How to get better answers from a model: write clearer prompts, ground it in trusted data, and adjust settings that make output more or less varied.

What the provider lists under it

• Overcoming foundation model limitations
• Prompt engineering techniques
• Advanced prompting techniques
• Grounding techniques
• Sampling parameters and settings

Terms worth knowing

• Prompt engineering: Writing instructions and context so an AI model gives a more useful answer.
• Retrieval-augmented generation (RAG): Letting a model look up relevant documents first and answer using them.
• Hallucination: When an AI model states something false or invented as if it were true.
• Tokens: The small chunks of text that a language model reads and writes, and that usage is measured in.
• Fine-tuning: Training an existing model a little more on your own examples so it behaves in the way you want.

The other Google Cloud Generative AI Leader domains

• Section 1: Fundamentals of gen AI (30% of the exam)
• Section 2: Google Cloud's gen AI offerings (35% of the exam)
• Section 4: Business strategies for a successful gen AI solution (15% of the exam)

Domain names, weights and topic lists come from the Google Cloud exam guide, checked 2 November 2026. Providers change their outlines, so confirm the current version before you book. The plain-English explanations are ours, not the provider’s. Not affiliated with, endorsed by or sponsored by any certification vendor or exam body. Certification names, exam codes and product names are trademarks of their respective owners and are used only to identify the exam a practice product prepares for. Our questions are original and are not actual exam questions.