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- AI
Retrieval Augmented Generation (RAG) for Developers
13 Courses
1 Lab
5 Hours
Skill IQ
Retrieval Augmented Generation (RAG) is an AI technique that combines the power of large language models with external knowledge retrieval to enhance the accuracy and relevance of generated content.
This path covers everything from modular RAGs and fine-tuning strategies to the role of vector space models and embeddings. The path also explores query understanding and expansion strategies, relevance ranking mechanisms, and techniques for integrating knowledge bases and external data sources.
Content in this path
Retrieval Augmented Generation (RAG) for Developers
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What You'll Learn
- How to deploy and maintain RAG systems
- How to evaluate RAG models
- How to optimize data retrieval techniques
- How to implement modular RAGs
- How to fine-tune RAGs
- How to utilize vector space models and embeddings in RAG
- How to develop query understanding and expansion strategies
- How to implement relevance ranking and scoring mechanisms
- How to integrate knowledge bases and external data sources
- How to implement multi-modal retrieval
- How to develop cross-lingual RAG systems
- How to perform retrieval augmented summarization and text simplification
- How to implement retrieval augmented fact verification
- How to scale RAG systems
- How to apply federated learning and privacy-preserving techniques in RAG
Prerequisites
- This path is intended for beginner learners and does not require any prerequisite knowledge.
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