Discourse with Soumith Chintala on Research and Applied Guidance (RAG), Search Techniques, and Artificial Intelligence at Perplexity
In the ever-evolving world of artificial intelligence (AI), a significant shift is taking place in the realm of search systems. The modern search landscape is a blend of various approaches, including vector embeddings, BM25 (an evolution of TF-IDF), semantic understanding, term-based retrieval, page authority, and recency. However, the Transformer architecture, developed in 2017, is not specifically mentioned in this context as being directly applied to AI search.
One of the most promising developments in this area is Retrieval-Augmented Generation (RAG), an AI technique that combines traditional information retrieval methods with large language model (LLM) generation capabilities. This hybrid approach enables models to fetch relevant external documents or data before generating responses, improving accuracy and grounding answers in up-to-date or domain-specific information.
The roots of RAG can be traced back to question-answering systems from the early 1970s, which applied natural language processing to search and extract answers from focused text corpora. Commercial question-answering became more mainstream in the mid-1990s with systems like Ask Jeeves (now Ask.com), and a major milestone was IBM’s Watson, which combined deep retrieval with language understanding to compete on Jeopardy! in 2011.
The specific term "Retrieval-Augmented Generation" was formally introduced in a 2020 research paper by Meta. This research emphasized combining retrieval from documents with LLM generation to overcome the limitations of static training data, enabling access to new, domain-specific, or updated information. RAG has since evolved from a research concept to a critical approach in industry applications, especially for data-intensive enterprises. Current implementations extend traditional text retrieval to multi-modal data, allowing AI to fetch and integrate text, images, charts, and diagrams into outputs.
RAG has transformed traditional search engines from simple keyword matching to generative search engines that provide direct, contextually rich answers rather than just ranked links. This represents a shift toward answering complex queries with precise, sourced information delivered as natural language responses—a blend of search and generation.
Key benefits of RAG include making responses current and authoritative by looking up information before answering, reducing hallucination by anchoring replies to specific documents or datasets, and reducing computational costs by retrieving updated knowledge on demand rather than permanently embedding it during training. RAG also enables applications such as customer support chatbots, enterprise knowledge bases, legal and technical document querying, and multimodal data usage (text + images).
As the future of AI search emphasizes creating the best user experience, which involves maintaining low latency, scaling to millions of queries, tracking performance at every level, and continuously optimizing for both speed and accuracy, RAG is poised to play a pivotal role. By bridging the gap between traditional search methods and advanced LLMs, RAG promises to deliver more accurate, transparent, and context-aware AI language and search systems.
Artificial Intelligence (AI) search is moving towards the integration of Retrieval-Augmented Generation (RAG), an AI technique that combines traditional information retrieval methods with large language model (LLM) generation capabilities, to improve accuracy and ground answers in up-to-date or domain-specific information. This development, formally introduced in a 2020 research paper by Meta, is expected to play a pivotal role in the future of technology, especially for data-intensive enterprises, as it promises to deliver more accurate, transparent, and context-aware AI language and search systems.
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