Production-Grade RAG Systems Engineering

What You Will Learn

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. 50+ hands-on labs, completely free.

16 modules, 31+ hands-on labs, 60+ hours, Beginner to Advanced, 100% free.

• 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

Full Curriculum

1. Module 1: Introduction to AI & RAG Systems

   LLM fundamentals, hallucinations, and why retrieval-augmented generation changes everything 3 hours. 2 hands-on labs.

   • Understand how LLMs work at a high level
   • Learn about tokens, context windows, and their limitations
   • Understand why LLMs hallucinate and how RAG solves it
   • Compare vanilla LLM vs RAG responses
2. Module 2: Foundations of Search & Retrieval

   BM25, TF-IDF, vector search fundamentals, and similarity metrics 3 hours. 2 hands-on labs.

   • Understand information retrieval fundamentals
   • Implement keyword search with BM25 and TF-IDF
   • Understand vector search and similarity metrics
   • Compare keyword vs semantic search tradeoffs
3. Module 3: Embeddings Deep Dive

   Embedding models, optimization strategies, and choosing the right model for your use case 3 hours. 2 hands-on labs.

   • Understand how text embedding models work
   • Compare embedding models and their tradeoffs
   • Optimize embeddings for production performance
   • Choose the right embedding strategy for your data
4. Module 4: Vector Databases Engineering

   ANN algorithms, indexing, metadata filtering, and production deployment with Qdrant and pgvector 4 hours. 2 hands-on labs.

   • Understand ANN algorithms (HNSW, IVF) and their tradeoffs
   • Deploy and operate Qdrant for production vector search
   • Use pgvector for PostgreSQL-integrated vector search
   • Design metadata filtering and multi-tenancy strategies
5. Module 5: Document Processing & Chunking

   Chunking strategies, data cleaning, metadata enrichment, and building ingestion pipelines 3.5 hours. 2 hands-on labs.

   • Design chunking strategies for different document types
   • Build robust document ingestion pipelines
   • Implement metadata enrichment for better retrieval
   • Handle PDFs, HTML, Markdown, and structured data
6. Module 6: Building Basic RAG Systems

   The complete retrieve-augment-generate pipeline with source attribution and citations 3.5 hours. 2 hands-on labs.

   • Build a complete RAG pipeline from scratch
   • Implement context injection and prompt augmentation
   • Add source attribution and citations
   • Handle edge cases: no results, conflicting sources, long context
7. Module 7: Advanced Retrieval Engineering

   Hybrid search, reranking, query expansion, and Graph RAG for production-quality retrieval 4 hours. 2 hands-on labs.

   • Implement hybrid search (BM25 + vector)
   • Add reranking with cross-encoder models
   • Design query expansion and transformation strategies
   • Understand Graph RAG for relationship-aware retrieval
8. Module 8: AI Agents & Agentic RAG

   Tool calling, memory systems, multi-agent architectures, and LangGraph orchestration 4 hours. 2 hands-on labs.

   • Build AI agents that reason and use tools
   • Implement agentic RAG with dynamic retrieval
   • Design multi-agent systems for complex tasks
   • Use LangGraph for agent orchestration
9. Module 9: Production RAG Architecture

   Scaling, multi-tenancy, caching, API gateways, and high-availability RAG deployments 4 hours. 2 hands-on labs.

   • Design scalable RAG architectures for production traffic
   • Implement multi-tenant retrieval with data isolation
   • Add caching layers for cost and latency optimization
   • Build production RAG APIs with FastAPI
10. Module 10: RAG Evaluation & Quality Engineering

    Hallucination detection, retrieval metrics, groundedness scoring, and evaluation frameworks 3.5 hours. 2 hands-on labs.

    • Measure retrieval quality with precision and recall
    • Detect and score hallucinations in generated answers
    • Build automated evaluation pipelines
    • Design continuous quality monitoring for production RAG
11. Module 11: AI Observability Engineering

    LLM tracing, token monitoring, cost tracking, and production AI telemetry 3 hours. 2 hands-on labs.

    • Instrument RAG systems with OpenTelemetry
    • Trace requests through the full RAG pipeline
    • Monitor token usage and LLM costs
    • Build AI-specific observability dashboards
12. Module 12: Security for RAG Systems

    Prompt injection defense, data leakage prevention, vector DB security, and AI access control 3.5 hours. 2 hands-on labs.

    • Defend against prompt injection attacks
    • Prevent data leakage across tenants
    • Secure vector database access with authentication
    • Implement AI-specific access control policies
13. Module 13: Deploying RAG Systems

    Dockerizing AI systems, Kubernetes for AI, GPU infrastructure, and CI/CD for AI applications 3.5 hours. 2 hands-on labs.

    • Containerize RAG systems with Docker
    • Deploy on Kubernetes with proper resource management
    • Configure GPU inference for embedding models
    • Build CI/CD pipelines for AI applications
14. Module 14: Advanced RAG Architectures

    Multimodal RAG, federated retrieval, personalized retrieval, and graph-based architectures 3.5 hours. 2 hands-on labs.

    • Build multimodal RAG with text + images
    • Design federated retrieval across multiple sources
    • Implement personalized retrieval based on user context
    • Architect graph-based retrieval for relational data
15. Module 15: AI Infrastructure & Future Systems

    MCP architecture, AI runtime systems, agent platforms, and workload identity for AI 3 hours. 2 hands-on labs.

    • Understand MCP (Model Context Protocol) architecture
    • Design AI runtime systems for production
    • Secure AI agents with workload identity (SPIFFE)
    • Build future-proof AI infrastructure
16. Module 16: Production Capstone Project

    Build a production-grade enterprise RAG platform with all components end-to-end 5 hours. 1 hands-on lab.

    • 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

Course Topics

RAG, LLM, Vector Database, Embeddings, Semantic Search, AI Agents, LangChain, LangGraph, Qdrant, pgvector, FastAPI, Python, AI Observability, Prompt Injection, Kubernetes, AI Security, Hybrid Search, Reranking, Graph RAG, MCP, Production AI, OpenTelemetry

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.

• Creator of DRF API Logger — 1.6M+ downloads, enterprise-grade API observability
• Author of 2 production-focused free courses (SPIFFE/SPIRE + Cloud Native Security)
• 80+ production-grade engineering tutorials at CodersSecret
• Production experience building AI retrieval systems at scale

Frequently Asked Questions

What is RAG?

RAG (Retrieval-Augmented Generation) is an AI architecture that retrieves relevant documents from a knowledge base and injects them into the LLM prompt. This grounds the model response in real data, dramatically reducing hallucinations and enabling domain-specific AI applications.

Is this course free?

Yes, 100% free. 16 modules, 50+ hands-on labs, all content and companion GitHub repository are completely free with no paywalls.

How is this different from LangChain tutorials?

Most RAG tutorials show you how to chain API calls. This course teaches production architecture: scalable retrieval, evaluation, observability, security, multi-tenancy, caching, and Kubernetes deployment. Framework-agnostic engineering, not framework-specific demos.

Do I need ML experience?

No. The course starts with LLM fundamentals and search basics, then progressively builds to advanced retrieval, agents, and production deployment. Python experience is sufficient.

What vector database does this course use?

Primarily Qdrant (open source, production-grade) and pgvector (PostgreSQL extension). The concepts apply to any vector database — Pinecone, Weaviate, Milvus, ChromaDB.

Will I build something real?

Yes. The capstone project is a production-grade enterprise RAG platform with document ingestion, hybrid retrieval, reranking, AI agents, observability, security, and Kubernetes deployment.

What is hybrid search?

Hybrid search combines keyword search (BM25) with semantic search (vector embeddings) for better retrieval quality. Keywords catch exact matches that semantic search misses, and semantic search catches meaning that keywords miss.

What about hallucinations?

Module 10 covers hallucination detection, groundedness evaluation, retrieval quality metrics, and techniques to minimize hallucinations through better retrieval, context management, and prompt design.