Mastering RAG
- Debajit Banerjee
- Mar 3
- 1 min read
Retrieval Augmented Generation (RAG) helps to provide additional context to enhance Large Language Model(LLM) responses by pulling in information from external databases or documents the user provides. An LLM based response is more pre-learned information whereas using RAG, each response now can be more specific, contextual, in-depth.
This attached E-book aims to be the go-to guide for all things RAG-related.
Target Audience: Machine Learning Engineer, Data Scientist, AI Researcher, Technical Product Manager
Click here to access the E-book
Reduce hallucinations, use advanced chunking techniques, select embedding and re-ranking models, choose a vector database, and much more
Overcome common challenges with building RAG systems
Get your system ready for production and improve performance
Document and Image Courtesy:
Galileo is the leading Generative AI Evaluation & Observability Stack for the Enterprise.
Contact: info@galileo.ai
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