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Everything about Retrieval-augmented generation

Type of information retrieval using LLMs

Retrieval-augmented generation (RAG) is a technique that enables large language models (LLMs) to retrieve and incorporate new information from external data sources. With RAG, LLMs first refer to a specified set of documents, then respond to user queries. These documents supplement information from the LLM's pre-existing training data. This allows LLMs to use domain-specific and/or updated information that is not available in the training data. For example, this enables LLM-based chatbots to access internal company data or generate responses based on authoritative sources. The technique was first proposed in 2020 and has since become a widely adopted approach in modern AI systems. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Retrieval-augmented generationA 5-minute orientation
  2. Skim a recent paper: Retrieval-Augmented Generation-Based Relation ExtractionInfluential work since 2022
  3. Pick up a classic book: The Art of Chunking in AI SystemsThe most-reprinted book on the subject
  4. Read the landmark paper: Graph Retrieval-Augmented Generation: A SurveyCited 192 times
  5. Go sideways: Prompt engineeringA closely related topic
  6. Go sideways: Information retrievalA closely related topic

📖 Definitions

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🗂️ The subject at a glance

  1. RAG and LLM limitations
  2. Process
  3. Applications
  4. Improvements
  5. Challenges

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics