RAG Explained: How Retrieval-Augmented Generation Actually Works
Retrieval-Augmented Generation (RAG) is a method that enhances the efficiency of large language models (LLMs) by separating the retrieval and generation processes. It utilizes an ingestion pipeline to process documents and a query pipeline to respond to user requests with relevant information. By using vector databases and chunking techniques, RAG ensures that only the most pertinent data is retrieved, minimizing costs and improving response quality.
- ▪RAG consists of two main pipelines: an ingestion pipeline for processing documents and a query pipeline for handling user requests.
- ▪The method addresses challenges such as cost, context limits, and quality by extracting only the most relevant chunks of information.
- ▪Vector databases are preferred over traditional text searches because they capture meaning and allow for efficient similarity searches.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1778532) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Suraj Sharma Posted on May 25 RAG Explained: How Retrieval-Augmented Generation Actually Works #ai #rag #llm #machinelearning The Two Phases of RAG RAG (Retrieval-Augmented Generation) splits into two separate pipelines: Ingestion pipeline — runs once (or on a schedule) to process your documents Query pipeline — runs live for every user request Why Not Just Send All Your Text to the LLM? Three hard problems: Cost — millions of tokens per query = $$$ Context limits — even 128K token…
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