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- AI
Retrieval Augmented Generation (RAG) for Developers
10 Courses
2 Labs
7 Hours
Skill IQ
This Retrieval Augmented Generation (RAG) path takes you from the core building blocks of grounded AI to running RAG systems in production. You’ll begin with embeddings, knowledge bases, and retrieval, then move on to tuning, evaluating, and structuring pipelines that stay accurate. From there, you’ll extend them to handle multiple languages, images, and sensitive data, and learn to deploy and maintain them at scale. Finish the path and you’ll be able to build, evaluate, and deploy reliable RAG applications for real projects.
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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