From 9b36dd90f52718a7d6f3e13de4c2ad5b2548ba17 Mon Sep 17 00:00:00 2001 From: bbustin Date: Fri, 24 Jan 2025 12:07:59 -0800 Subject: [PATCH] Update README.md Fixed a typo in the README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 559f3c0d..60c0ccb2 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # RAG From Scratch -LLMs are trained on a large but fixed corpus of data, limiting their ability to reason about private or recent information. Fine-tuning is one way to mitigate this, but is often [not well-suited for facutal recall](https://www.anyscale.com/blog/fine-tuning-is-for-form-not-facts) and [can be costly](https://www.glean.com/blog/how-to-build-an-ai-assistant-for-the-enterprise). +LLMs are trained on a large but fixed corpus of data, limiting their ability to reason about private or recent information. Fine-tuning is one way to mitigate this, but is often [not well-suited for factual recall](https://www.anyscale.com/blog/fine-tuning-is-for-form-not-facts) and [can be costly](https://www.glean.com/blog/how-to-build-an-ai-assistant-for-the-enterprise). Retrieval augmented generation (RAG) has emerged as a popular and powerful mechanism to expand an LLM's knowledge base, using documents retrieved from an external data source to ground the LLM generation via in-context learning. These notebooks accompany a [video playlist](https://youtube.com/playlist?list=PLfaIDFEXuae2LXbO1_PKyVJiQ23ZztA0x&feature=shared) that builds up an understanding of RAG from scratch, starting with the basics of indexing, retrieval, and generation. ![rag_detail_v2](https://github.com/langchain-ai/rag-from-scratch/assets/122662504/54a2d76c-b07e-49e7-b4ce-fc45667360a1)