AI Termcirca 2020· Added Jun 5, 2026
Retrieval-Augmented Generation (RAG)
RAG is a technique that combines information retrieval with language generation models to improve the accuracy and relevance of AI outputs.
Retrieval-Augmented Generation (RAG) is an approach that integrates the strengths of traditional information retrieval systems with modern language generation models. By incorporating a retrieval mechanism, RAG enhances the ability of AI systems to generate more accurate and contextually relevant responses. The retrieval component sources pertinent data from a large corpus, which is then used by a language model to generate responses that are informed by real-world information, rather than relying solely on pre-trained knowledge. This method is particularly useful in domains where up-to-date or specific information is critical, such as customer support and research.
Examples
- A customer service chatbot using RAG to fetch real-time product availability information.
- An AI tool generating legal advice by retrieving current laws and regulations before crafting responses.
- A content creation assistant using RAG to pull recent news articles for generating reports.
Common misconceptions
- RAG does not replace the need for a robust language model; it complements it.
- RAG is not suitable for every application; it excels where access to current or specific data is needed.
- RAG cannot inherently verify the accuracy of the retrieved data, it only enhances contextual relevance.
Related terms
Want more like this?
Open the full library
Fresh AI mastery content every 2 hours.