RAG Mastery

Understand the fundamental concepts of RAG and how it addresses limitations of traditional LLMs.

• Explain RAG architecture and workflow
• Compare RAG with fine-tuning and prompt engineering

Master the technologies that power efficient semantic search in RAG systems.

• Implement various embedding models and evaluate their performance
• Set up and query vector databases (Pinecone, Chroma, FAISS)

Network with fellow participants and instructors, discussing real-world RAG applications.

Learn effective strategies for processing and chunking documents for optimal retrieval.

• Extract text from various document formats (PDF, HTML, etc.)
• Implement semantically meaningful chunking methods

Develop techniques to optimize query processing for more accurate retrievals.

• Implement query understanding and expansion techniques
• Build a basic RAG pipeline with effective query processing

Master sophisticated retrieval techniques to enhance RAG system performance.

• Implement re-ranking strategies for improved results
• Apply multi-step reasoning and query decomposition

Learn to combine multiple search methods for more robust retrieval systems.

• Integrate keyword and semantic search for better results
• Develop ensemble methods for combining different retrieval approaches

Continue discussions and networking with peers and instructors.

Learn comprehensive frameworks for evaluating and improving RAG systems.

• Apply evaluation metrics for retrieval and generation quality
• Set up automated evaluation pipelines for continuous improvement

Apply best practices for deploying scalable, reliable RAG systems in production.

• Implement caching, indexing, and performance optimization
• Design for scalability, monitoring, and continuous updates

Review key learnings and discuss advanced topics for further exploration.

• Receive course completion certificate
• Access community resources and support for ongoing learning