Advanced RAG Systems
Build production-grade RAG pipelines — covering GraphRAG, agentic RAG, hybrid retrieval, RAGAS evaluation, and vector database selection. Course with certificate — try 2 lessons free.
85% of enterprise AI applications now use RAG. But most are stuck at “naive RAG” — basic vector search over chunked documents that hallucinates, misses context, and breaks on complex queries.
Stanford’s 2025 study found that legal RAG tools hallucinate 17-33% of the time despite their RAG claims. The gap between “has RAG” and “has RAG that works” is engineering, not magic.
This course closes that gap. You’ll learn the advanced patterns that separate production RAG from demo RAG: hybrid retrieval reaching 91% accuracy, contextual chunking that reduces retrieval failures by 67%, agentic RAG that self-corrects, and evaluation frameworks that catch hallucinations before users do.
- The seven-layer production RAG architecture and when to use each component
- Chunking strategies backed by 2025 benchmarks — including when simple fixed-size beats sophisticated semantic
- Hybrid retrieval (BM25 + dense + sparse) with cross-encoder reranking for 91% accuracy
- Advanced patterns: GraphRAG for relationship queries, agentic RAG for multi-step reasoning, corrective RAG for self-healing
- RAGAS evaluation: faithfulness, context precision, answer relevancy — automated and continuous
- Production infrastructure: vector database selection, embedding model benchmarks, caching architectures, and monitoring
What You'll Learn
- Design multi-stage RAG architectures with query transformation, hybrid retrieval, and reranking pipelines
- Implement advanced chunking strategies (semantic, parent-child, contextual) and evaluate their impact on retrieval quality
- Build hybrid search systems combining dense embeddings, sparse vectors, and BM25 for 91% retrieval accuracy
- Apply GraphRAG, agentic RAG, and corrective RAG patterns to solve multi-hop reasoning problems
- Evaluate RAG systems using RAGAS metrics — faithfulness, context precision, answer relevancy — with automated testing
- Implement production RAG with vector database selection, embedding model optimization, caching, and monitoring
After This Course, You Can
What You'll Build
Course Syllabus
Who Is This For?
- Backend developers building RAG features into production applications
- ML engineers optimizing retrieval quality and reducing hallucination
- AI engineers designing enterprise knowledge systems
- Technical leads evaluating RAG architecture decisions
Frequently Asked Questions
What's the format — video or text?
Text-based. Each lesson is a written walkthrough with copy-paste prompts you can run in ChatGPT, Claude, or Gemini, plus code snippets, architecture diagrams, and worked examples. There are no video lectures. Lessons 1 and 2 are free, so you can check the format before subscribing.
What prerequisites do I need?
You should understand basic RAG concepts (embedding, retrieval, generation), have Python experience, and be familiar with at least one LLM API (OpenAI, Anthropic, etc.). This is an advanced course — start with our Prompt Engineering for Developers course if you're new to RAG.
Which vector databases does this course cover?
We compare Pinecone, Weaviate, Qdrant, Chroma, and pgvector — with benchmarks and use-case recommendations for each. You don't need paid accounts to follow along.
Is this course about theory or implementation?
Implementation-focused. Every lesson includes code examples, architecture diagrams, and production considerations. You'll design a complete enterprise RAG system in the capstone.
How is this different from your Prompt Engineering for Developers course?
Prompt Engineering covers RAG basics in one lesson. This course goes deep: 8 lessons on advanced chunking, hybrid retrieval, GraphRAG, evaluation frameworks, and production deployment. It picks up where that lesson left off.
