Introduction to RAG
RAG stands for Retrieval-Augmented Generation. It’s a clever setup where a transformer model doesn’t just make things up — it actually goes out, finds real information, and brings it back before answering.
How RAG Works
In this process, 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 Process Step-by-Step
- Encoding: The user’s question and internal documents are encoded using DPR.
- Embeddings: Two sets of embeddings are produced from the encoding process.
- Similarity Measures: FAISS compares the embeddings to find the most relevant passages.
- Retrieval: The retrieved, relevant context is then passed to a generator model.
- Response Generation: The generator model produces a precise and informed response.
Example Use Case
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 seen before. RAG enables the AI to find this information and provide an accurate response.
Conclusion
RAG is a powerful tool that enables AI models to provide more accurate and informed responses by retrieving relevant information from internal documents and procedures. This technology has the potential to revolutionize the way we interact with AI assistants and improve the overall user experience.
FAQs
- What does RAG stand for?: RAG stands for Retrieval-Augmented Generation.
- How does RAG work?: RAG uses Dense Passage Retrieval (DPR) to encode the user’s question and internal documents, and then uses FAISS to compare the embeddings and find the most relevant passages.
- What is the benefit of using RAG?: The benefit of using RAG is that it enables AI models to provide more accurate and informed responses by retrieving relevant information from internal documents and procedures.
