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AI Glossary Series: Retrieval Augmented Generation (RAG) [AI Today Podcast]
Jan 24, 2024
Learn about retrieval augmented generation (RAG) and its advantages. Discover how RAG systems combine relevant information with a user's query. Explore strategies for minimizing hallucinations in RAG systems. Expand your AI knowledge with the AI Today podcast and website.
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Quick takeaways
- Retrieval Augmented Generation (RAG) constrains large language models (LLMs) to only consider specific data sources, ensuring more accurate and tailored responses.
- RAG enables contextually accurate and detailed responses by combining relevant context with user queries, offering benefits for domain-specific applications.
Deep dives
What is Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) is a concept in the context of large language models (LLMs) that allows for more specific and relevant responses. LLMs are powerful but rely on general training data, which may not be domain-specific or up-to-date. RAG addresses this by constraining the LLM to only consider information from specific data sources provided in the prompt. This ensures that the generated content is contextual, accurate, and relevant to the specific data sets.
![AI Glossary Series: Retrieval Augmented Generation (RAG) [AI Today Podcast]](https://www.cognilytica.com/wp-content/uploads/2024/01/RAG-150x150.png)
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