Build practical skills for designing, grounding, and improving generative AI applications with Google Cloud Vertex AI. In this Specialization, you’ll learn how prompt design, Gemini models, embeddings, vector search, and retrieval-augmented generation (RAG) work together to produce more useful and grounded AI experiences.
You’ll begin with generative AI and large language model fundamentals, then explore prompt engineering and Vertex AI Studio. As you progress, you’ll work with text and multimodal embeddings, grounding techniques, controlled generation, and multimodal Gemini use cases. You’ll then apply these concepts to semantic search, hybrid search, multimodal RAG, and LLM-powered application architectures.
By the end, you’ll be able to:
Design effective prompts for generative AI applications.
Ground model responses using retrieval, embeddings, and your own data.
Build semantic and hybrid search workflows with Vertex AI Vector Search.
Develop multimodal and RAG-based applications using Gemini and Vertex AI.
This Specialization is designed for developers, cloud practitioners, and technical learners who want hands-on experience building generative AI solutions with Google Cloud.
Applied Learning Project
Throughout this Specialization, you’ll apply generative AI concepts in hands-on Google Cloud labs and application-building activities. You’ll prototype Gemini experiences in Vertex AI Studio, explore text and multimodal embeddings, generate structured model responses, and work through multimodal use cases involving documents, images, video, audio, and code. You’ll also build retrieval workflows using semantic and hybrid search, create a vector-powered search experience with Vertex AI Vector Search, perform multimodal RAG, and build an LLM- and RAG-based chat application. These activities help you connect prompt design, grounding, retrieval, and application architecture in realistic generative AI scenarios.
