Module 16: Production Capstone Project Slides
Slide walkthrough for Module 16 of Production-Grade RAG Systems Engineering: Build a production-grade enterprise RAG platform with all components...
This slide page is the visual review companion for the full course module. Use it to recap the architecture, examples, exercises, production warnings, and takeaways after reading the lesson.
Slide Outline
- Production Capstone Project - Build a production-grade enterprise RAG platform with all components end-to-end
- Learning Objectives - 4 outcomes for this module
- Why This Module Matters - This capstone proves you can architect and deploy a complete production AI system — not just chain API calls. It is the
- What You Build - Lesson section from the full module
- Technology Stack - Lesson section from the full module
- This Is Your Portfolio Piece - Lesson section from the full module
- Hands-On Labs - 1 hands-on lab
- Key Takeaways - 5 points to remember
Learning Objectives
- Build a complete enterprise RAG platform
- Integrate all components: ingestion, retrieval, generation, security, observability
- Deploy on Kubernetes with full production architecture
- Test with realistic enterprise scenarios
Why This Module Matters
This capstone proves you can architect and deploy a complete production AI system — not just chain API calls. It is the difference between "I built a chatbot" and "I engineered a production RAG platform." That distinction matters for career advancement.
Key Takeaways
- Production RAG = ingestion + retrieval + generation + security + observability + deployment
- Every component from Modules 1-15 integrates into a cohesive platform
- Quality gates in CI/CD prevent regression on every change
- Security is not optional — prompt injection and data leakage are real threats
- This capstone is your proof of production RAG engineering competence
Hands-On Labs
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Capstone: Production RAG Platform
Build and deploy the full enterprise RAG platform.
3 hours - Advanced
- Build document ingestion pipeline
- Deploy Qdrant with hybrid search and reranking
- Build FastAPI API with streaming and caching
- Add AI agents with tool calling
- Implement evaluation and hallucination detection
- Add OpenTelemetry observability
- Implement prompt injection defense and tenant isolation
- Deploy on Kubernetes with CI/CD quality gates
- Run end-to-end tests with realistic enterprise queries
- Document architecture decisions