What is Retrieval-Augmented Generation (RAG)?
IBM Technology
6 min, 36 sec
Marina Danilevsky introduces the concept of Retrieval-Augmented Generation (RAG) to enhance the accuracy and timeliness of responses from large language models.
Summary
- Marina Danilevsky, Senior Research Scientist at IBM Research, explains RAG for improving LLMs.
- RAG combines retrieval of current information with generation to provide accurate answers.
- Danilevsky uses the example of identifying the planet with the most moons to demonstrate RAG's utility.
- RAG addresses issues of outdated information and lack of sources in LLMs by retrieving up-to-date content.
- Ongoing IBM research focuses on improving both the retriever and generator components of RAG.
Chapter 1
Marina Danilevsky introduces the concept of Retrieval-Augmented Generation and its relevance to large language models.
- Marina Danilevsky is a Senior Research Scientist at IBM Research.
- She introduces Retrieval-Augmented Generation (RAG) as a solution to improve LLMs.
- RAG is designed to make LLMs more accurate and up-to-date.
Chapter 2
The generation aspect of LLMs is explained, focusing on how they respond to prompts and their limitations.
- Generation refers to LLMs creating text in response to prompts.
- These models can exhibit undesirable behavior, such as providing outdated or unsourced information.
Chapter 3
Danilevsky illustrates LLM limitations through a personal story about answering her children's question regarding the solar system.
- She uses an anecdote about planets and moons to highlight problems with LLMs.
- The anecdote shows how confidence in an answer does not equate to accuracy.
Chapter 4
RAG's approach to addressing the challenges of outdated information and lack of sources in LLMs is explained.
- RAG combines real-time data retrieval with generation for more accurate responses.
- The retrieval process involves consulting a content store before responding to a prompt.
Chapter 5
The mechanics of how the RAG framework operates are detailed, including the user's interaction with the model.
- The user prompts the LLM, which originally would generate a response based on training.
- With RAG, the LLM first retrieves relevant content before generating an answer.
Chapter 6
The benefits of using RAG for LLMs are discussed, along with ongoing research to further enhance the framework.
- RAG allows LLMs to stay up-to-date without retraining and to cite sources, reducing the likelihood of hallucinations.
- LLMs can admit when they don't know an answer, avoiding misinformation.
- IBM continues to improve the retriever and generator components of RAG.
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