[100% Off] Retrieval-Augmented Generation (Rag) Systems Practice Tests
Master RAG architecture, vector search, chunking & evaluation to build production-grade retrieval-augmented LLM apps
What you’ll learn
- Understand core RAG concepts — retrieval
- embeddings
- vector search
- chunking
- and how RAG reduces hallucination and grounds LLM outputs,Design and evaluate retrieval strategies including hybrid search
- re-ranking
- query transformation
- and parent-child retrieval patterns,Architect production-grade RAG systems: indexing pipelines
- multi-tenant security
- API design
- latency optimization
- and scalability,Debug and optimize RAG systems using faithfulness metrics
- A/B testing
- cost optimization
- and continuous production monitoring
Requirements
- Basic familiarity with how large language models work (e.g.
- what a prompt is) is helpful. No prior experience with vector databases
- embeddings
- or RAG-specific tooling is required — this course builds those concepts from the ground up. Some comfort with general software or AI concepts will help you get the most out of the material
- but you dont need to be a machine learning engineer to succeed here.
Description
Retrieval-Augmented Generation (RAG) has become the backbone of how modern LLM applications access current, proprietary, and domain-specific knowledge — but most engineers learn it through scattered tutorials that stop at a basic demo. This course goes far deeper.
Through 600 scenario-based practice questions across six comprehensive tests, you’ll build a working, production-level understanding of what it actually takes to design, build, evaluate, and operate a real RAG system.
You’ll cover:
RAG Fundamentals & Core Concepts — what RAG is, why it exists, hallucination reduction, RAG vs. fine-tuning vs. prompt engineering
Embeddings, Vector Search & Similarity — embedding models, vector databases, cosine similarity, ANN search (HNSW), sparse vs. dense retrieval
Retrieval Strategies & Chunking — chunking strategies, hybrid search, re-ranking, query transformation, parent-child retrieval
RAG Architecture & System Design — indexing pipelines, multi-tenant security, API design, latency and cost optimization, scalability
Evaluation, Optimization & Debugging — faithfulness and relevance metrics, A/B testing, failure diagnosis, continuous production monitoring
Advanced Techniques & Production Deployment — agentic RAG, GraphRAG, multi-modal RAG, long-context tradeoffs, deployment patterns like canary and shadow testing
Every question comes with a full explanation, so you understand the reasoning behind each answer — not just the correct choice. Whether you’re adding RAG to an existing LLM application, architecting a new system from scratch, or preparing for technical interviews in this space, this course gives you the depth to build RAG systems that actually work well in production.
