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Advanced RAG Architecture, Custom Skills & Evaluation on Google Cloud Platform

Target Audience: Security Professionals, Cloud Architects, and Python Developers
Prerequisites: Proficiency in Python, basic familiarity with GCP (Vertex AI), and core understanding of LLM/RAG concepts
Format: 4-Week Intensive / Hands-on Workshop Style

Executive Summary

Organizations are increasingly leveraging Retrieval-Augmented Generation (RAG) to automate complex analytical tasks—such as evaluating Data Flow Diagrams (DFDs) against strict technical requirements, security baselines, diagramming standards, and internal SDLC handbooks. Moving from a basic vector search proof-of-concept to an enterprise-grade, secure, and highly accurate production system presents significant engineering challenges.

This course directly addresses the key knowledge gaps:

  1. Accuracy & Data Management — Hybrid, chunking-aware, and structured domain retrieval.
  2. Storage Architecture Selection — Evaluating vector stores, graph databases, and relational/hybrid options on Google Cloud Platform.
  3. Skill & Prompt Engineering — Modular LLM “Skills” (tools/functions) without context-window bloat.
  4. Automated Evaluation & CI/CD Testing — Continuous verification of faithfulness, relevance, context recall, and security compliance.

Learning Objectives

By the end of this course you will be able to:

  • Architect hybrid RAG pipelines with custom chunking, metadata enrichment, and hybrid search (Vector + Keyword + Knowledge Graph) tailored to multi-document alignment (DFDs + Security Standards + SDLC).
  • Select and provision optimal GCP storage options (Vertex AI Vector Search, pgvector on Cloud SQL / AlloyDB, BigQuery, Neo4j / Spanner Graph).
  • Develop single-responsibility agentic skills in Python without prompt bloat or tool overload.
  • Implement automated RAG evaluation pipelines using Ragas, Vertex AI Evaluation, and deterministic assertions, integrated into Cloud Build / GitHub Actions quality gates.

Course Structure at a Glance

Week Focus Key Labs
1 Precision RAG & Domain Chunking Document-aware chunking, Hybrid retrieval + re-ranking
2 Storage Architecture on GCP AlloyDB pgvector vs Vertex AI Vector Search, Graph RAG
3 Skill Architecture & Prompt Hygiene Modular Function Calling, Dynamic Skill Router
4 Automated Evaluation & CI/CD Golden datasets + Ragas, Cloud Build quality gates
Capstone Automated DFD Security & SDLC Compliance Evaluator End-to-end production pipeline

How to Use This Site

  • Navigate via the top tabs or left sidebar.
  • Each week contains an Overview, detailed Theory, and hands-on Lab pages.
  • Lab code and notebooks live in the repository under /labs and /notebooks.
  • The Capstone section contains the full project specification and starter template guidance.

Ready to begin? Start with the Syllabus or jump straight into Week 1.