This is a reply from kapa.ai (ref)
RAG stands for Retrieval Augmented Generation. It’s a technique used in AI models to examine the latest user input and the context of the conversation, and then use embeddings or other search techniques to fill the AI model context with specialized knowledge relevant to the topic. This is usually used for making an AI that can answer about closed-domain problems, such as a company knowledgebase.
The phrase Retrieval Augmented Generation (RAG) comes from a recent paper (https://arxiv.org/abs/2005.11401) by Lewis et al. from Facebook AI. The idea is to use a pre-trained language model (LM) to generate text, but to use a separate retrieval system to find relevant documents to condition the LM on.
In practice, RAG can be combined with tools like Elasticsearch or Qdrant to enhance the performance of AI models. It can also be fine-tuned for specific use-cases, and can be used in conjunction with Few-Shot Learning to boost the model’s performance and reduce hallucinations.
Sources:
- Forum Post: Difference between langchain and rag > Official Answer (https://community.openai.com/t/528625)
- Examples > Fine-tuned_qa > Fine-Tuning for Retrieval Augmented Generation (RAG) with Qdrant (https://cookbook.openai.com/examples/fine-tuned_qa/ft_retrieval_augmented_generation_qdrant)
- Docs > Guides > Prompt engineering > Use external tools (https://platform.openai.com/docs/guides/prompt-engineering)