From 5b9432de6b45a32535c25a4b036e4b8518c2934e Mon Sep 17 00:00:00 2001 From: vtasca <56837288+vtasca@users.noreply.github.com> Date: Thu, 14 Sep 2023 13:35:11 +0200 Subject: [PATCH] Update rag.en.mdx Correct usage of "its" instead of "it's". --- pages/techniques/rag.en.mdx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pages/techniques/rag.en.mdx b/pages/techniques/rag.en.mdx index 4ca7f88..95fce46 100644 --- a/pages/techniques/rag.en.mdx +++ b/pages/techniques/rag.en.mdx @@ -7,7 +7,7 @@ General-purpose language models can be fine-tuned to achieve several common task For more complex and knowledge-intensive tasks, it's possible to build a language model-based system that accesses external knowledge sources to complete tasks. This enables more factual consistency, improves reliability of the generated responses, and helps to mitigate the problem of "hallucination". -Meta AI researchers introduced a method called [Retrieval Augmented Generation (RAG)](https://ai.facebook.com/blog/retrieval-augmented-generation-streamlining-the-creation-of-intelligent-natural-language-processing-models/) to address such knowledge-intensive tasks. RAG combines an information retrieval component with a text generator model. RAG can be fine-tuned and it's internal knowledge can be modified in an efficient manner and without needing retraining of the entire model. +Meta AI researchers introduced a method called [Retrieval Augmented Generation (RAG)](https://ai.facebook.com/blog/retrieval-augmented-generation-streamlining-the-creation-of-intelligent-natural-language-processing-models/) to address such knowledge-intensive tasks. RAG combines an information retrieval component with a text generator model. RAG can be fine-tuned and its internal knowledge can be modified in an efficient manner and without needing retraining of the entire model. RAG takes an input and retrieves a set of relevant/supporting documents given a source (e.g., Wikipedia). The documents are concatenated as context with the original input prompt and fed to the text generator which produces the final output. This makes RAG adaptive for situations where facts could evolve over time. This is very useful as LLMs's parametric knowledge is static. RAG allows language models to bypass retraining, enabling access to the latest information for generating reliable outputs via retrieval-based generation. @@ -22,4 +22,4 @@ This shows the potential of RAG as a viable option for enhancing outputs of lang More recently, these retriever-based approaches have become more popular and are combined with popular LLMs like ChatGPT to improve capabilities and factual consistency. -You can find a [simple example of how to use retrievers and LLMs for question answering with sources](https://python.langchain.com/docs/use_cases/question_answering/how_to/vector_db_qa) from the LangChain documentation. \ No newline at end of file +You can find a [simple example of how to use retrievers and LLMs for question answering with sources](https://python.langchain.com/docs/use_cases/question_answering/how_to/vector_db_qa) from the LangChain documentation.