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AI theory, architecture, models
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Module 18
Retrieval-Augmented Generation (RAG)
1
Dense Retrieval with Sentence Embeddings and Vector Databases
Implement a dense retrieval system using sentence embeddings and a vector database
2
Building a RAG Pipeline
Build a complete RAG pipeline that combines retrieval with generation
3
Cross-Encoder Reranking for Document Retrieval
Implement a cross-encoder model for reranking retrieved documents
4
Contrastive Learning for Sentence Embeddings
Apply contrastive learning to train high-quality sentence embedding models (bi-encoders)
5
Evaluating RAG Performance: Retrieval and Generation Metrics
Evaluate RAG systems using retrieval (e.g., recall) and generation metrics
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LLM Interaction: Prompting and In-Context Learning
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Agentic AI Systems