Outcome 1
A production-ready RAG architecture with ingestion, chunking, embedding, retrieval, reranking, and answer generation
Free course
Production-Grade RAG Systems Engineering
Build scalable, reliable, observable, and secure Retrieval-Augmented Generation systems — not another chatbot tutorial
Outcomes
What you will be able to build and explain
Each outcome is tied to architecture, operational judgement, or a concrete deployment habit you can reuse at work.
Outcome 2
A vector-search backend using Qdrant or pgvector with filtering, metadata, and hybrid retrieval
Outcome 3
Evaluation workflows for retrieval quality, groundedness, hallucination checks, and regression testing
Outcome 4
Observability for prompts, retrieval latency, context quality, token cost, and user-facing failures
Outcome 5
Security controls for prompt injection, tenant isolation, data leakage, and tool permissions
Outcome 6
A deployable FastAPI RAG service with caching, streaming, CI/CD checks, Docker, and Kubernetes deployment notes
Learning loop
Learn the model, practice the decision, keep the checklist
The most practical production-focused RAG engineering course available. Not another chatbot demo — this is how real-world scalable, reliable, observable, secure RAG systems are designed and operated. 16 modules covering embeddings, vector databases (Qdrant, pgvector), hybrid retrieval, reranking, AI agents (LangGraph), evaluation, observability, prompt injection defense, and Kubernetes deployment. 31 hands-on labs, completely free.
01
Inspect the architecture
Start every module with the system model: components, trust boundaries, data flow, and the production problem it solves.
02
Practice the failure mode
Labs and exercises focus on the operational edge cases that separate tutorial knowledge from production confidence.
03
Ship with judgement
Production notes, common mistakes, and tradeoffs make the course useful when you are designing or reviewing real systems.
Good fit
Who should take this course?
This course is written for engineers who need practical production context, not abstract theory.
Backend engineers building AI-powered applications
Python developers entering AI systems engineering
AI engineers moving from prototypes to production
DevOps engineers deploying AI infrastructure
Platform engineers building AI-ready platforms
Software architects designing retrieval systems
Developers who are tired of shallow chatbot tutorials
Curriculum
Full course path
16 modules, 31 hands-on labs, 50+ hours of production-focused learning.
Instructor
Vishal Anand
Senior Product Engineer & Tech Lead
Creator of DRF API Logger (1.6M+ PyPI downloads), educator at CodersSecret, and author of the Mastering SPIFFE & SPIRE and Cloud Native Security Engineering courses. Builds production AI and infrastructure systems.
FAQ
Questions before you start
Topics