Build a Production-Style RAG System — From Data to Deployment
Architecture. Orchestration. Guardrails. Observability. FastAPI Deployment.
Most engineers understand RAG. Very few can design one under production constraints.
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Demo-level knowledge is no longer enough. Engineering teams need people who understand how RAG systems behave under production constraints: latency budgets, cost optimization, failure modes, and observability requirements.
Senior interviews evaluate system thinking. They want to know if you can articulate trade-offs, design for scale, and build pipelines that don't break when real users start sending queries.
Production capability separates engineers. This program gives you the structured experience to design, implement, and defend a RAG architecture with confidence.
Build production-ready skills across the full RAG pipeline
Design end-to-end RAG architecture
Implement intelligent chunking
Build multi-vector retrieval
Add Redis caching
Orchestrate the pipeline
Implement guardrails
Instrument observability
Deploy with FastAPI
Build a Supabase-backed production project
Production-ready resources and access
Complete implementations from data to deployment
Structured guidance across all RAG layers
Full repository with working implementations
FastAPI setup and production patterns
Production safety and monitoring frameworks
All materials, updates, and future improvements
Software Engineers
ML Engineers
Backend Engineers
AI Builders
Engineers preparing for senior interviews
This program is not designed for beginners looking for a 'What is RAG?' introduction.
This program helps you build production pipelines.
Engineers who want to move deeper into system design, multi-agent architectures, evaluation loops, and cost governance typically continue into the Agentic AI Enterprise Bootcamp.
Nachiketh Murthy is the Instructor at Manifold AI Learning, and a visionary AI educator on a mission to create 1 million AI leaders. Known for his expertise in Agentic AI, Generative AI, MLOps, and enterprise-scale deployments, he has mentored thousands of learners and professionals worldwide. His teaching blends technical depth with practical application, empowering learners to master next-gen AI systems while building a strong career path.
If this program doesn't meet your expectations within the first 7 days, request a full refund. No questions asked.