Implementing RAG from Scratch Using Hugging Face Transformers and FAISS
Last Updated on May 6, 2025 by Editorial Team
Author(s): Jayita Gulati
Originally published on Towards AI.
In the world of AI and chatbots, it’s exciting to see language models generate human-like text. But there’s one big problem: these models don’t always know everything. They only respond based on the data they were trained on. What if you want your model to answer questions from your custom documents, company wikis, or recent articles?
That’s where Retrieval-Augmented Generation (RAG) comes in.
RAG helps language models become smarter and more useful by letting them “look things up” before answering. This article will walk you through building a RAG pipeline using popular open-source tools: Hugging Face Transformers, FAISS for similarity search, and SentenceTransformers for encoding.
Retrieval-Augmented Generation (RAG) is a method that improves how language models answer questions by letting them retrieve relevant information from external sources before generating a response.
Traditional models like GPT or BERT generate text based only on what they learned during training, which means they can’t access new or dynamic data unless retrained. This is a major limitation, especially in real-world applications like customer support or medical Q&A.
RAG solves this by breaking the process into two parts:
Retrieve — Search for relevant document chunks related to the user’s question.Generate — Use a language model (like BART or T5)… Read the full blog for free on Medium.
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