This course covers knowledge grounding, vector search, agent workflows, and scalable deployment of RAG-powered agents for real-world enterprise applications.
Overview
Vertex AI RAG Agents Training focuses on building intelligent, enterprise-ready AI agents using Retrieval-Augmented Generation (RAG) on Google Vertex AI. This course covers knowledge grounding, vector search, agent workflows, and scalable deployment of RAG-powered agents for real-world enterprise applications.
Learning Outcomes
• Understand the fundamentals of RAG (Retrieval-Augmented Generation) and AI agents using Vertex AI
• Build AI agents capable of retrieving and generating contextual responses from enterprise data
• Integrate vector databases, embeddings, and knowledge sources into RAG workflows
• Develop scalable AI agent architectures using Vertex AI tools and APIs
• Apply prompt engineering, orchestration, and workflow automation techniques for RAG systems
• Understand security, governance, and responsible AI practices for enterprise AI agents
Duration & Delivery Mode
14 hours
Target Audience
• AI engineers and ML practitioners
• Cloud developers and solution architects
• Enterprise AI and innovation teams
• Data engineers working with knowledge systems
• Technical leads building AI agents
Pre-requisites
• Understanding of large language models and generative AI
• Familiarity with Google Cloud and Vertex AI basics
• Basic knowledge of APIs or application development
Skillset Achieved
• Designing RAG architectures on Vertex AI
• Implementing vector search and knowledge grounding
• Building autonomous and semi-autonomous AI agents
• Optimizing response accuracy and relevance
• Deploying and monitoring RAG agents in production
Course Outcome
By the end of this training, participants will be able to design, build, and deploy scalable RAG-powered AI agents using Vertex AI, enabling accurate, grounded, and enterprise-ready generative AI solutions.
Course Outline
Foundations of RAG and AI Agents
• RAG concepts and enterprise use cases
• Agent-based architectures and workflows
• Vertex AI tools for RAG
Knowledge Ingestion and Vector Search
• Document ingestion and preprocessing
• Embeddings and vector databases
• Semantic retrieval strategies
Prompting and Context Management
• Prompt patterns for RAG systems
• Context window optimization
• Reducing hallucinations
Building RAG-Powered Agents
• Agent orchestration and decision logic
• Tool usage and action planning
• Multi-step reasoning workflows
Deployment and Scaling
• Deploying RAG agents on Vertex AI
• Performance tuning and latency optimization
• Cost management strategies
Responsible AI and Governance
• Data privacy and security
• Evaluation and monitoring of agent outputs
• Enterprise governance considerations
Assessment Topics
• Fundamentals of RAG architecture and AI agent workflows
• Vertex AI integration with vector databases and enterprise data
• Embedding models and contextual retrieval techniques
• Prompt engineering and agent orchestration concepts
• Security, governance, and responsible AI considerations
• Practical hands-on RAG agent development exercises
Evaluation
• RAG pipeline implementation exercises
• Agent workflow design tasks
• Final hands-on assessment
Course Materials
Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.
Certification
Participants will receive an AcadNXT Certification in Vertex AI RAG Agents Training, validating their expertise in building retrieval-augmented AI agents on Google Vertex AI.
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What Our Students Say
“Excellent deep dive into RAG and agent workflows.”
“The vector search and grounding sections were outstanding.”
“Very practical approach to enterprise RAG agents.”
“Clear, structured, and highly relevant training.”
“Helped us build reliable, production-ready AI agents.”