Vertex AI RAG Agents Training in Thimphu, Bhutan

The Vertex AI RAG Agents Training is a hands-on program designed to teach how to build Retrieval-Augmented Generation (RAG) systems and AI agents using Google Cloud Vertex AI.

The course focuses on designing knowledge-grounded, reliable, and scalable AI agents that can retrieve information from enterprise data sources, reason over it, and take actions—while maintaining security, governance, and production readiness.

Introduction to RAG & Agentic AI on Vertex AI

• Why RAG is critical for enterprise AI
• RAG vs fine-tuning
• Role of agents in RAG systems
• Vertex AI RAG ecosystem overview

Vertex AI Architecture for RAG Agents

• Vertex AI generative models
• Embedding models and vector stores
• Agent orchestration components
• End-to-end RAG system design

Data Ingestion & Knowledge Pipelines

• Document ingestion patterns
• Chunking strategies
• Metadata and tagging
• Incremental updates and re-indexing

Embeddings & Vector Search on Vertex AI

• Creating embeddings
• Vector indexes and similarity search
• Tuning recall and precision
• Hybrid search (keyword + vector) concepts

Retrieval Strategies & Query Understanding

• Query rewriting and expansion
• Multi-query retrieval
• Filtering with metadata
• Re-ranking strategies

Prompting for RAG Systems

• Prompt templates for grounded answers
• Context assembly strategies
• Citation-aware prompting
• Reducing hallucinations with grounding

Building RAG-Powered AI Agents

• Agent roles and responsibilities
• Agent + RAG orchestration patterns
• Tool calling with retrieved context
• Multi-step agent workflows

Knowledge Validation & Answer Verification

• Source attribution and citations
• Confidence scoring concepts
• Cross-checking retrieved content
• Handling missing or conflicting information

Tool & System Integration

• Connecting to enterprise systems
• APIs and structured data sources
• Search, databases, and document stores
• Secure tool access for agents

Evaluation of RAG & Agent Systems

• Retrieval quality metrics
• Answer faithfulness and groundedness
• Automated evaluation pipelines
• Human review workflows

Performance & Cost Optimization

• Index sizing and sharding
• Caching strategies
• Token optimization
• Cost monitoring and limits

Security, Privacy & Data Governance

• Access control for knowledge sources
• Handling sensitive enterprise data
• Data segregation and tenancy
• Compliance and audit readiness

Monitoring & Observability

• Tracking retrieval and generation metrics
• Detecting drift in knowledge bases
• Logging agent actions
• Alerting and troubleshooting

Production Deployment Patterns

• CI/CD for RAG pipelines
• Environment separation (dev/test/prod)
• Rollback and re-index strategies
• Versioning prompts and indexes

Hands-on Exercises

Summary and Conclusion