Dive into Learning Explore the rich courses offerings of MammothClub.

Test your knowledge of prompt engineering and grounding techniques for generative AI! Including the definition and importance of prompt engineering, common and advanced prompting techniques such as zero-shot, few-shot, role prompting, chain-of-thought, and ReAct, and when to use them; grounding concepts using first-party, third-party, and world data; the impact of retrieval-augmented generation (RAG) on model outputs; Google Cloud grounding options like Vertex AI Search, RAG APIs, and Google Search grounding; and how sampling parameters such as temperature, token limits, top-p, safety settings, and output length control model behavior.

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Test your knowledge of how to assess business and technical requirements, select the appropriate foundation models, and configure models effectively to meet performance, cost, security, and scalability goals.

Test your knowledge of implementing data validation and processing pipelines for foundation model consumption, and designing and implementing vector store solutions to enable efficient retrieval, grounding, and scalable generative AI applications.

Test your knowledge of designing retrieval mechanisms to augment foundation models, and implementing prompt engineering strategies and governance to ensure safe, reliable, and consistent foundation model interactions.

Test your knowledge of implementing foundation model deployment strategies and enterprise integration architectures! Including deploying foundation models based on application and performance needs, addressing large language model deployment challenges, optimizing performance and resource usage, integrating generative AI into existing enterprise systems, designing secure and compliant access frameworks, enabling cross-environment and hybrid deployments, and implementing CI/CD pipelines and GenAI gateway architectures for scalable, secure, and controlled enterprise adoption.