computer-scienceangular-roadmapbackend-roadmapblockchain-roadmapdba-roadmapdeveloper-roadmapdevops-roadmapfrontend-roadmapgo-roadmaphactoberfestjava-roadmapjavascript-roadmapnodejs-roadmappython-roadmapqa-roadmapreact-roadmaproadmapstudy-planvue-roadmapweb3-roadmap
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
9 lines
973 B
9 lines
973 B
# RAG |
|
|
|
Retrieval-Augmented Generation (RAG) is an AI approach that combines information retrieval with language generation to create more accurate, contextually relevant outputs. It works by first retrieving relevant data from a knowledge base or external source, then using a language model to generate a response based on that information. This method enhances the accuracy of generative models by grounding their outputs in real-world data, making RAG ideal for tasks like question answering, summarization, and chatbots that require reliable, up-to-date information. |
|
|
|
Learn more from the following resources: |
|
|
|
- [@article@What is Retrieval Augmented Generation (RAG)?](https://www.datacamp.com/blog/what-is-retrieval-augmented-generation-rag) |
|
- [@article@What is Retrieval-Augmented Generation? Google](https://cloud.google.com/use-cases/retrieval-augmented-generation) |
|
- [@video@What is Retrieval-Augmented Generation? IBM](https://www.youtube.com/watch?v=T-D1OfcDW1M) |