Course Includes:
- Price: FREE
- Enrolled: 0 students
- Language: English
- Certificate: Yes
- Difficulty: Advanced
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Course Folder
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.
Who this course is for:
- This course is for engineers
- AI/ML practitioners
- and technical builders who want to design
- implement
- and troubleshoot Retrieval-Augmented Generation systems — whether youre adding RAG to an existing LLM application
- architecting a new one from scratch
- or preparing for technical interviews in this space. Its especially useful if youve experimented with basic RAG tutorials but want a deeper
- systematic understanding of retrieval strategy
- production architecture
- and evaluation that goes beyond a quick demo.
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