{"id":25802,"date":"2023-11-23T14:42:51","date_gmt":"2023-11-23T13:42:51","guid":{"rendered":"https:\/\/wordlift.io\/blog\/en\/?post_type=entity&p=25802"},"modified":"2023-11-23T16:16:28","modified_gmt":"2023-11-23T15:16:28","slug":"retrieval-augmented-generation","status":"publish","type":"entity","link":"https:\/\/wordlift.io\/blog\/en\/entity\/retrieval-augmented-generation\/","title":{"rendered":"Retrieval Augmented Generation (RAG)"},"content":{"rendered":"\n

The concept of \u201chuman in the loop\u201d<\/strong> primarily concerns the quality of the data used to craft prompts for AI systems. To ensure fair and scalable AI systems, WordLift utilizes the Retrieval Augmented Generation (RAG)<\/strong> approach. <\/p>\n\n\n\n

RAG combines a retriever and a generator to enhance the capabilities of Large Language Models (LLMs). The retriever scours the knowledge graph to find relevant information, which the generator then uses to craft accurate and contextually precise responses. This approach addresses the limitations of traditional LLM usage by providing up-to-date and reputable information. You can learn more about RAG and its benefits in the article here<\/a>.<\/p>\n\n\n\n

Prompt engineering is a technique used in machine learning to provide additional information or insights to improve the predictions of a model. It involves including this information in the prompt used during training or fine-tuning. Prompt engineering is considered a form of technical SEO and can be used to enhance the capabilities of AI systems in generating content. To learn more about prompt engineering and its application in natural language generation, you can read this article: ChatGPT Prompt Engineering: NLG – A Detailed Overview<\/a>.<\/p>\n\n\n\n

\"\"<\/a><\/figure>\n\n\n\n

<\/p>\n\n\n","protected":false},"excerpt":{"rendered":"

The concept of \u201chuman in the loop\u201d primarily concerns the quality of the data used to craft prompts for AI systems. To ensure fair and scalable AI systems, WordLift utilizes the Retrieval Augmented Generation (RAG) approach. RAG combines a retriever and a generator to enhance the capabilities of Large Language Models (LLMs). The retriever scours … Continued<\/a><\/p>\n","protected":false},"author":65,"featured_media":25837,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"wl_entities_gutenberg":"","_wlpage_enable":"","footnotes":""},"categories":[28],"wl_entity_type":[12],"coauthors":[],"class_list":["post-25802","entity","type-entity","status-publish","has-post-thumbnail","hentry","category-world-summit-ai","wl_entity_type-thing"],"acf":[],"yoast_head":"\nRetrieval Augmented Generation (RAG) - WordLift Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/wordlift.io\/blog\/en\/entity\/retrieval-augmented-generation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Retrieval Augmented Generation (RAG) - WordLift Blog\" \/>\n<meta property=\"og:description\" content=\"The concept of \u201chuman in the loop\u201d primarily concerns the quality of the data used to craft prompts for AI systems. 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