Kernekoncepter
Retrieval-Augmented Generation (RAG) is a powerful technique that combines large language models with external knowledge sources to generate more informative and accurate responses, reducing hallucinations.
Resumé
This article provides a comprehensive overview of building retrieval-augmented generation systems. It covers the following key points:
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Introduction to RAG:
- RAG is a technique that combines large language models with external knowledge sources to generate more informative and accurate responses.
- The concept of RAG was first published in a 2020 paper by Lewis et al. and has gained significant interest since the release of ChatGPT.
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Naive RAG Paradigm:
- Naive RAG consists of two stages: ingestion and inference.
- In the ingestion stage, an external knowledge source is prepared.
- In the inference stage, the retrieved context and user query are used to augment a prompt template, which is then used to generate an answer.
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Advanced RAG Paradigms:
- Since naive RAG has some limitations, advanced RAG paradigms have emerged.
- These advanced paradigms introduce new concepts and techniques to improve the performance and capabilities of RAG systems.
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Living Document:
- This article is intended as a central place and a curated collection of articles on building retrieval-augmented generation systems.
- The article will be regularly updated to keep it current with the latest developments in the field.