[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-191562-105":53,"doc-detail-191562-en":127},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":120,"head_meta":122,"extra_data":124,"updated_unix":126},105,"en","rag-components-workflow-and-steps","RAG Components, Workflow, and Steps","","This document details the RAG (Retrieval-Augmented Generation) framework, outlining its core components, workflow, and specific steps. The RAG Components section breaks down the process into Indexing, Retrieval, and Generation. The RAG Workflow elaborates on Pre-Retrieval, Retrieval, Post-Retrieval, and Generation stages. The RAG Steps provides granular details for each phase, including Indexing, Data Modification, Query Manipulation, Search Ranking, Re-Ranking, Customization, Summarization, and Synthesization within Multi-Step Reduction. The document also presents comparative performance metrics on various datasets (PubMedQA, ELI5, Long-context QA, Sec 10-Q) evaluating a \"Venn Diagram Prompt\" against a \"Standard Prompt\" across metrics such as Answer Relevancy, Answer Similarity, Answer Correctness, and LLM Judge scores, indicating that the Venn Diagram Prompt generally performs comparably or 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are the main components of the RAG framework?","Question",{"text":109,"@type":110},"The RAG framework comprises three main components: Indexing, Retrieval, and Generation.","Answer",{"name":112,"@type":107,"acceptedAnswer":113},"What does the RAG workflow involve?",{"text":114,"@type":110},"The RAG workflow follows a sequence of Pre-Retrieval, Retrieval, Post-Retrieval, and finally, Generation.",{"name":116,"@type":107,"acceptedAnswer":117},"How does the Venn Diagram Prompt compare to the Standard Prompt?",{"text":118,"@type":110},"Performance metrics indicate that the Venn Diagram Prompt generally performs comparably or better than the Standard Prompt across various datasets and evaluation metrics.","https://schema.org",{"og:url":78,"og:type":121,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":123,"canonical":78},"index,follow",{"doc_id":125,"site_id":56},191562,1789982289,{"code":4,"msg":5,"data":128},{"doc_id":125,"user_id":129,"nickname":88,"user_avatar":130,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":101,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":136,"language":137,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":138,"faqs":139,"seo_title":140,"seo_description":61,"update_tm":141,"read_time":79},687197207639,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","| Datasets | Document Type | Average\u003Cbr>Context Length |\n| --- | --- | --- |\n| ELI5 | Single-Document, Multi-Chunk | 65235 |\n| PubMedQA | Single-Document, Multi-Chunk | 4500 |\n| Long-context QA | Single-Document, Single-Chunk | 18762.38 |\n| Sec 10-Q | Multi-Document | 650000 |\n\n\n|  | Answer\u003Cbr>Relevancy | Answer\u003Cbr>Similarity | Answer\u003Cbr>Correctness |\n| --- | --- | --- | --- |\n| PubMedQA |  |  |  |\n| VD Prompt | 0.9597 | 0.9172 | 0.5353 |\n| Standard Prompt | 0.9427 | 0.8690 | 0.3838 |\n| ELI5 |  |  |  |\n| VD Prompt | 0.9027 | 0.9026 | 0.4466 |\n| Standard Prompt | 0.9056 | 0.8904 | 0.3880 |\n| Long Context QA |  |  |  |\n| VD Prompt | 0.9549 | 0.9118 | 0.8017 |\n| Standard Prompt | 0.9465 | 0.9083 | 0.7517 |\n| Sec 10-Q |  |  |  |\n| VD Prompt | 0.9353 | 0.9471 | 0.5512 |\n| Standard Prompt | 0.9273 | 0.9575 | 0.6951 |\n\n|  | LLM Judge 1 | LLM Judge 2 |\n| --- | --- | --- |\n| PubMedQA |  |  |\n| VD Prompt | 7.8182 | 3.7273 |\n| Standard Prompt | 6.7273 | 2.4545 |\n| ELI5 |  |  |\n| VD Prompt | 7.454 | 3.1818 |\n| Standard Prompt | 6.9000 | 2.7000 |\n| Long Context QA |  |  |\n| VD Prompt | 8.4545 | 3.7273 |\n| Standard Prompt | 7.7273 | 3.1818 |\n| Sec 10-Q |  |  |\n| VD Prompt | 8.4737 | 3.4737 |\n| Standard Prompt | 8.1000 | 3.7000 |","cbCaiqEVSPsp0XAi","https://ap.wps.com/l/cbCaiqEVSPsp0XAi","pdf",636439,10,"English","# RAG Components\n## Indexing\n## Retrieval\n## Generation\n# RAG Workflow\n## Pre Retrieval\n## Retrieval\n## Post-Retrieval\n## Generation\n# RAG Steps\n## Indexing\n## Data Modification\n## Query Manipulation\n## Search Ranking\n## Re-Ranking\n## Customization\n## Summarization\n## Synthesization\n## Multi-Step Reduction\n## Venn Diagram LLM Prompt","[{\"question\":\"What are the main components of the RAG framework?\",\"answer\":\"The RAG framework comprises three main components: Indexing, Retrieval, and Generation.\"},{\"question\":\"What does the RAG workflow involve?\",\"answer\":\"The RAG workflow follows a sequence of Pre-Retrieval, Retrieval, Post-Retrieval, and finally, Generation.\"},{\"question\":\"How does the Venn Diagram Prompt compare to the Standard Prompt?\",\"answer\":\"Performance metrics indicate that the Venn Diagram Prompt generally performs comparably or better than the Standard Prompt across various datasets and evaluation metrics.\"}]","RAG Components, Workflow, and Steps | PDF",1788409345]