A Deep Technical Exploration of Retrieval-Augmented Generation (RAG) with Transformers, DPR, FAISS, and BART

Author(s): Saif Ali Kheraj

Originally published on Towards AI.

RAG stands for Retrieval-Augmented Generation. It’s a clever setup where a transformer model (you know, the brains behind all gpts) doesn’t just make things up — it actually goes out, finds real information, and brings it back before answering.

In this post, I will walk you through how it all works, step by step. Dense Passage Retrieval (DPR) plays a key role — it performs smart encoding using models trained on question–answer datasets. DPR uses a BERT-based encoder that processes text starting with tokenization, then applies embeddings, attention mechanisms, and multiple transformer layers to produce final vector representations (embeddings). We apply this encoding to both the user’s question and the internal documents or paragraphs. This results in two sets of embeddings. To find the most relevant passages, we use FAISS (developed by Facebook), which compares these embeddings using similarity measures. The retrieved, relevant context is then passed to a generator model, which produces a precise and informed response.

Someone asks your AI assistant, “How should I store fragile items in the warehouse?”

The answer is not in a public blog or textbook — it is buried deep inside your internal warehouse manuals and handling procedures, which the AI model has never… Read the full blog for free on Medium.

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Published via Towards AI