[{"data":1,"prerenderedAt":378},["ShallowReactive",2],{"$fgukOamtKU1RtUiMFsqdObttmqPPQz0uc7bl_gj_LyX0":3,"$ft-oOWlxoJyQ4Y6pHXiiqvwGk4SRGbvnDlwQwf43HFm4":245,"article-356":377},{"code":4,"msg":5,"data":6},0,"",{"category":7,"tag":11,"popular":19,"latest":86,"banner":126,"list":151,"cache":244},[8,9,10],"Agent","OpenAI","LLM",[8,12,13,14,9,10,15,16,17,18],"Google","Nvidia","Claude","DeepSeek","OCR","Chat","Generator",[20,29,37,45,54,62,70,79],{"id":21,"publish_date":22,"is_original":23,"collection":5,"cover_url":24,"cover_url_1_1":25,"title":26,"summary":27,"author":28},411,"2023-09-10",1,"article_res/cover/451ef50c225a8dc61c4336506794d13b.jpeg","article_res/cover/3ba9dc7a72f87d40b20fc2d225289ee3.jpeg","Idealism","Reality is created by the mind, we can change our reality by changing our mind. - Plato","Renee's Entrepreneurial Journey",{"id":30,"publish_date":31,"is_original":23,"collection":32,"cover_url":33,"cover_url_1_1":34,"title":35,"summary":36,"author":28},108,"2024-12-07","#LLM #AGI #AI Agent","article_res/cover/0039044422e4ec9f61c18e8ee1693bb0.jpeg","article_res/cover/4220971b108a91d21407d87bb02fbaa6.jpeg","Freysa.ai: The World's First Adversarial AI Agent Game","说服 Freysa 把钱包里的钱都拿出来",{"id":38,"publish_date":39,"is_original":23,"collection":40,"cover_url":41,"cover_url_1_1":42,"title":43,"summary":44,"author":28},12,"2025-03-09","#Oxford #Reasoning #LLM #Tool Use","article_res/cover/d448e9b3617a0b5302e1bd10c438bca9.jpeg","article_res/cover/864a468f9cc4c9317efadb3811909888.jpeg","Agentic Reasoning Framework - Significantly enhance the reasoning ability of LLMs through the integration of external tools using agents","Agentic Reasoning: Reasoning LLMs with Tools for Deep Research",{"id":46,"publish_date":47,"is_original":4,"collection":48,"cover_url":49,"cover_url_1_1":50,"title":51,"summary":52,"author":53},480,"2023-04-14","#Stable Diffusion","article_res/cover/0bdbe7cb1de4a78e54536e5d9afa7ec9.jpeg","article_res/cover/b3d6ffec0608dcfaf18c5a69906d1490.jpeg","【AIGC Learning】Generate Prompts Using Word Graphs - Stable Diffusion Web UI Series 13","AI will become a powerful tool in education, transforming the way we learn and deliver instruction.  \n- Reid Hoffman","--",{"id":55,"publish_date":56,"is_original":4,"collection":57,"cover_url":58,"cover_url_1_1":59,"title":60,"summary":61,"author":28},413,"2023-09-08","#Neuroscience","article_res/cover/74f8302d78a23d9430f22171eae136b6.jpeg","article_res/cover/87ca08af81bb304746be5261160964c0.jpeg","Can machines be conscious?","Do we have an ethical obligation to not turn off conscious machines? Would turning them off be murder? No. I don't lose any sleep over unplugging a conscious machine.\n- Jeff Hawkins, \"A Thousand Brains\"",{"id":63,"publish_date":64,"is_original":23,"collection":65,"cover_url":66,"cover_url_1_1":67,"title":68,"summary":69,"author":28},178,"2024-09-09","#Entrepreneurship","article_res/cover/a7224f025b55d1820408085faef63079.jpeg","article_res/cover/11a9995b096cbf64465ef01b8673b154.jpeg","37signals company","This damn sense of relaxation",{"id":71,"publish_date":72,"is_original":4,"collection":73,"cover_url":74,"cover_url_1_1":75,"title":76,"summary":77,"author":78},460,"2023-05-12","#Google","article_res/cover/b970687b12faa52da976f91248c2aa7b.jpeg","article_res/cover/d1e71b52cfd2c63bc6e71f3e85ff135c.jpeg","Learn what BRC-20 and Ordinals are using Google Bard","Ordinals - a new protocol that allows users to store arbitrary data on the Bitcoin blockchain","Google Bard mainly writes",{"id":80,"publish_date":81,"is_original":23,"collection":5,"cover_url":82,"cover_url_1_1":83,"title":84,"summary":85,"author":28},309,"2024-03-26","article_res/cover/9877f95894ee88532d0e6012c23a2df3.jpeg","article_res/cover/20092164ddc109ce6ae56b1984246751.jpeg","Learning the Cancun Upgrade with lepton and perplexity","Building a quick conversation-based search demo with Lepton AI.",[87,95,103,111,119],{"id":88,"publish_date":89,"is_original":23,"collection":90,"cover_url":91,"cover_url_1_1":92,"title":93,"summary":94,"author":28},627,"2025-03-20","#AI Avatar #AI Video Generation","article_res/cover/d95481358f73924989f8c4ee9c75d1c8.jpeg","article_res/cover/b74bc0fab01f8b6a6aa87696c0c3ed8b.jpeg","DisPose: Generating Animated Videos by Driving Video with Reference Images","DisPose is a controllable human image animation method that enhances video generation.",{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},626,"2025-03-21","#Deep Dive into LLMs #LLM #RL #Andrej Karpathy #AlphaGo","article_res/cover/446553a5c8f8f2f07d97b20eaee84e56.jpeg","article_res/cover/e6c2823409c9b34624064b9acbaca6f1.jpeg","AlphaGo and the Power of Reinforcement Learning - Andrej Karpathy's Deep Dive on LLMs (Part 9)","Simply learning from humans will never surpass human capabilities.",{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},625,"2025-03-22","#Deep Dive into LLMs #LLM #RL #RLHF #Andrej Karpathy","article_res/cover/8da81d38b1e5cf558a164710fd8a5389.jpeg","article_res/cover/96f028d76c362a99a0dd56389e8f7a9b.jpeg","Reinforcement Learning from Human Feedback (RLHF) - Andrej Karpathy's Deep Dive on LLMs (Part 10)","Fine-Tuning Language Models from Human Preferences",{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},624,"2025-03-23","#Deep Dive into LLMs #LLM #Andrej Karpathy #AI Agent #MMM","article_res/cover/a5e7c3d48bb09109684d6513287c661d.jpeg","article_res/cover/d3f22b7c0ab8d82fd2da457a299e0773.jpeg","The Future of Large Language Models - Andrej Karpathy's In-Depth Explanation of LLM (Part 11)","preview of things to come",{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},623,"#Google #Voe #AI Video Generation","article_res/cover/c44062fea0f336c2b96b3928292392c2.jpeg","article_res/cover/a041041c69092ad3db191c5bf3ff981b.jpeg","Trial of Google's video generation model VOE2","Our state-of-the-art video generation model",[127,135,143],{"id":128,"publish_date":129,"is_original":23,"collection":130,"cover_url":131,"cover_url_1_1":132,"title":133,"summary":134,"author":28},300,"2024-04-16","#AI in Science #AGI","article_res/cover/6bf01e793e0f33e848572412eebdf9b0.jpeg","article_res/cover/91a5ee21dafecb914fabeb9430d46ec1.jpeg","Would Einstein lose his job - AI and Quantum Computing: A Glimpse into the Near Future","So Einstein's job is still safe.",{"id":136,"publish_date":137,"is_original":23,"collection":138,"cover_url":139,"cover_url_1_1":140,"title":141,"summary":142,"author":28},101,"2024-12-14","#Nvidia #AI 3D Generator","article_res/cover/693e07c85980c5c0c8fde3f037733f23.jpeg","article_res/cover/9ea8edff2d5d303ff3fffff3f6f9c3d9.jpeg","NVIDIA's open-source 3D project LLaMA-Mesh","LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models",{"id":144,"publish_date":145,"is_original":23,"collection":146,"cover_url":147,"cover_url_1_1":148,"title":149,"summary":150,"author":28},131,"2024-11-10","#OpenAI","article_res/cover/87f8ed353ce39f31960e7cdfaf075a35.jpeg","article_res/cover/f597a63935f5cd32e484b4aadd6019e8.jpeg","ChatGPT has launched the Search function","Get fast, timely answers with links to relevant web sources.",{"big":152,"small":214},[153,181],{"title":154,"list":155},"AGENT",[156,157,165,173],{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},{"id":158,"publish_date":159,"is_original":23,"collection":160,"cover_url":161,"cover_url_1_1":162,"title":163,"summary":164,"author":28},622,"2025-03-24","#OWL #AI Agent #MAS #MCP #CUA","article_res/cover/cb50ca7f2bf4d1ed50202d7406e1c19a.jpeg","article_res/cover/4aa7aa3badfacf3cc84121334f1050dd.jpeg","OWL: Multi-agent collaboration","OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation",{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},620,"2025-03-26","#LLM #Google #Gemini #AI Agent","article_res/cover/53751a6dbbe990b1eb0b63f3b062aed4.jpeg","article_res/cover/031344981f0a212ff82d1f3a64aa5756.jpeg","Gemini 2.5 Pro, claimed to be far ahead of the competition, has been released with great fanfare: comprehensively surpassing other LLMs and topping the global rankings","Gemini 2.5: Our most intelligent AI model",{"id":174,"publish_date":175,"is_original":23,"collection":176,"cover_url":177,"cover_url_1_1":178,"title":179,"summary":180,"author":28},616,"2025-03-29","#MAS #AI Agent #AI Coder #MetaGPT #MGX","article_res/cover/9dcd702ad2035902e5e77967c34a1f1e.jpeg","article_res/cover/0a97fc4a922753c8f46ff38792020df8.jpeg","MGX - An automated website-building platform composed of multiple AI Agents","Your 24/7 AI Team | Dream, Chat, Create.",{"title":182,"list":183},"OPENAI",[184,191,199,206],{"id":185,"publish_date":167,"is_original":23,"collection":186,"cover_url":187,"cover_url_1_1":188,"title":189,"summary":190,"author":28},619,"#OpenAI #AI Image Generator #4o #MMM #AR Transformer","article_res/cover/2faffc97fcecf3151552cb0fd3206d89.jpeg","article_res/cover/1133cb4948af44cee2e7fbe79efb69e5.jpeg","The native image function of GPT-4o is officially launched","Introducing 4o Image Generation",{"id":192,"publish_date":193,"is_original":4,"collection":194,"cover_url":195,"cover_url_1_1":196,"title":197,"summary":198,"author":28},434,"2023-07-15","#Anthropic #OpenAI #Google #AI Code Generator #Claude","article_res/cover/e1b6f600a2b9f262a4392684e5f2ce25.jpeg","article_res/cover/6e1772e83f78f9a351ab23d3e414adee.jpeg","Latest Updates on Google Bard /Anthropic Claude2 / ChatGPT Code Interpreter","We want our models to use their programming skills to provide more natural interfaces to the basic functions of our computers.  \n - OpenAI",{"id":200,"publish_date":201,"is_original":4,"collection":146,"cover_url":202,"cover_url_1_1":203,"title":204,"summary":205,"author":28},417,"2023-08-24","article_res/cover/bccf897d50a88b18364e35f7466387e0.jpeg","article_res/cover/2f871085c1073717c1703ae86e18056f.jpeg","The GPT-3.5 Turbo fine-tuning (fine-tuning function) has been released～","Developers can now bring their own data to customize GPT-3.5 Turbo for their use cases.",{"id":207,"publish_date":208,"is_original":4,"collection":209,"cover_url":210,"cover_url_1_1":211,"title":212,"summary":213,"author":28},407,"2023-09-22","#OpenAI #AI Image Generator","article_res/cover/c59005e903d35cfc32346e2756e2728a.jpeg","article_res/cover/ba011d265e6d84b5c8cb6fd6b757b6cc.jpeg","Dall-E 3","DALL·E 3 understands significantly more nuance and detail, allowing you to easily translate your ideas into images.",[215,221,241],{"title":10,"list":216},[217,218,219,220],{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"title":222,"list":223},"GOOGLE",[224,225,226,234],{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"id":227,"publish_date":228,"is_original":23,"collection":229,"cover_url":230,"cover_url_1_1":231,"title":232,"summary":233,"author":28},615,"2025-03-30","#AI Researcher #AI Science #HKU #Google #AI Agent","article_res/cover/21fadf906067714bb0db31ae13a77c15.jpeg","article_res/cover/2697999a72bd26b22e85f0e92936d3ed.jpeg","AI-Researcher: LLM-driven全自动 scientific research assistant","AI-Researcher: Fully-Automated Scientific Discovery with LLM Agents  \nOpen-Sourced Alternative to Google AI Co-Scientist",{"id":235,"publish_date":236,"is_original":23,"collection":73,"cover_url":237,"cover_url_1_1":238,"title":239,"summary":240,"author":28},463,"2023-05-09","article_res/cover/89800f207723acdb55fc53bf999ebdc9.jpeg","article_res/cover/5764f369b4accd8f83e94aa4c077a175.jpeg","The Smallville sandbox world - A town with 25 virtual residents","Believable proxies of human behavior can empower interactive apps: Immersive environment, Rehearsal space, Prototyping tool",{"title":242,"list":243},"NVIDIA",[],true,{"code":4,"msg":5,"data":246},{"id":247,"publish_date":248,"is_original":23,"collection":249,"articles_id":250,"cover_url":251,"cover_url_1_1":252,"title":253,"summary":254,"author":28,"content":255,"popular":256,"list":319,"category":375,"tag":376},356,"2023-12-29","#Prompt Engineering #LLM #Langchain","gCkIksW6DsfwQYU9ZOAZAQ","article_res/cover/b0f8821a7f4dc973dab6a6cd5c8af14a.jpeg","article_res/cover/47f95d0ae22c01db9fc24a4a3380f202.jpeg","How to make LLMs smarter","This guide shares strategies and tactics for getting better results from large language models.","\u003Cdiv class=\"rich_media_content js_underline_content\n                       autoTypeSetting24psection\n            \" id=\"js_content\">\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>Today, the HR of our company made a sharing about \"Making Better Decisions Through Cognitive Psychology,\" which has many similarities to the \"Business Analysis and Decision Making\" course I attended with Professor Wu at NUS.\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>When I previously researched how to make LLMs smarter, I also discovered similar approaches.\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>Large language models are probabilistic models. If you let it answer questions quickly, it will adopt System 1 (fast thinking).\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003122\" data-ratio=\"0.9733727810650887\" data-s=\"300,640\" data-type=\"png\" data-w=\"338\" style=\"\" src=\"./assets/17423813087560.10877495218727762.png\">\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>There are multiple ways to make existing large language models (LLMs) smarter, which can be roughly divided into four categories:\u003C/p>\u003Ch2 data-tool=\"mdnice编辑器\" style='margin-top: 30px;margin-bottom: 15px;font-weight: bold;font-size: 22px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;letter-spacing: normal;text-align: left;text-wrap: wrap;'>1. Ask better questions\u003C/h2>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>The training process of LLMs uses a vast amount of data, far exceeding the books we read and the content we find online.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003123\" data-ratio=\"0.4334705075445816\" data-s=\"300,640\" data-type=\"png\" data-w=\"729\" style=\"\" src=\"./assets/17423813087780.08157224407328756.png\">\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>Therefore, asking good questions can help better leverage the potential of LLMs. There are many books and websites that provide guidance on how to construct question prompts (Prompt), and the basic logic is as follows:\u003C/p>\u003Csection data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;color: rgb(0, 0, 0);font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;overflow-x: auto;'>\u003Ctable>\u003Cthead>\u003Ctr style=\"border-width: 1px 0px 0px;border-right-style: initial;border-bottom-style: initial;border-left-style: initial;border-right-color: initial;border-bottom-color: initial;border-left-color: initial;border-top-style: solid;border-top-color: rgb(204, 204, 204);background-color: white;\">\u003Cth style=\"border-top-width: 1px;border-color: rgb(204, 204, 204);background-color: rgb(240, 240, 240);min-width: 85px;text-align: left;\">Elements\u003C/th>\u003Cth style=\"border-top-width: 1px;border-color: rgb(204, 204, 204);background-color: rgb(240, 240, 240);min-width: 85px;text-align: left;\">Name\u003C/th>\u003Cth style=\"border-top-width: 1px;border-color: rgb(204, 204, 204);background-color: rgb(240, 240, 240);min-width: 85px;text-align: left;\">Description\u003C/th>\u003C/tr>\u003C/thead>\u003Ctbody style=\"border-width: 0px;border-style: initial;border-color: initial;\">\u003Ctr style=\"border-width: 1px 0px 0px;border-right-style: initial;border-bottom-style: initial;border-left-style: initial;border-right-color: initial;border-bottom-color: initial;border-left-color: initial;border-top-style: solid;border-top-color: rgb(204, 204, 204);background-color: white;\">\u003Ctd style=\"border-color: rgb(204, 204, 204);min-width: 85px;\">Task\u003C/td>\u003Ctd style=\"border-color: rgb(204, 204, 204);min-width: 85px;\">Task\u003C/td>\u003Ctd style=\"border-color: rgb(204, 204, 204);min-width: 85px;\">The type of content that GPT needs to generate\u003C/td>\u003C/tr>\u003Ctr style=\"border-width: 1px 0px 0px;border-right-style: initial;border-bottom-style: initial;border-left-style: initial;border-right-color: initial;border-bottom-color: initial;border-left-color: initial;border-top-style: solid;border-top-color: rgb(204, 204, 204);background-color: rgb(248, 248, 248);\">\u003Ctd style=\"border-color: rgb(204, 204, 204);min-width: 85px;\">Instruction\u003C/td>\u003Ctd style=\"border-color: rgb(204, 204, 204);min-width: 85px;\">Instructions\u003C/td>\u003Ctd style=\"border-color: rgb(204, 204, 204);min-width: 85px;\">Principles to follow when generating content\u003C/td>\u003C/tr>\u003Ctr style=\"border-width: 1px 0px 0px;border-right-style: initial;border-bottom-style: initial;border-left-style: initial;border-right-color: initial;border-bottom-color: initial;border-left-color: initial;border-top-style: solid;border-top-color: rgb(204, 204, 204);background-color: white;\">\u003Ctd style=\"border-color: rgb(204, 204, 204);min-width: 85px;\">Character\u003C/td>\u003Ctd style=\"border-color: rgb(204, 204, 204);min-width: 85px;\">Role\u003C/td>\u003Ctd style=\"border-color: rgb(204, 204, 204);min-width: 85px;\">The role that GPT needs to play\u003C/td>\u003C/tr>\u003Ctr style=\"border-width: 1px 0px 0px;border-right-style: initial;border-bottom-style: initial;border-left-style: initial;border-right-color: initial;border-bottom-color: initial;border-left-color: initial;border-top-style: solid;border-top-color: rgb(204, 204, 204);background-color: rgb(248, 248, 248);\">\u003Ctd style=\"border-color: rgb(204, 204, 204);min-width: 85px;\">Keywords\u003C/td>\u003Ctd style=\"border-color: rgb(204, 204, 204);min-width: 85px;\">Seed-word\u003C/td>\u003Ctd style=\"border-color: rgb(204, 204, 204);min-width: 85px;\">Points to emphasize\u003C/td>\u003C/tr>\u003C/tbody>\u003C/table>\u003C/section>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>\u003Cstrong>For example:\u003C/strong>\u003C/p>\u003Cblockquote data-tool=\"mdnice编辑器\" style='margin-top: 20px;margin-bottom: 20px;padding: 10px 10px 10px 20px;border-left-color: rgba(0, 0, 0, 0.4);color: rgb(106, 115, 125);font-size: 0.9em;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;letter-spacing: normal;text-align: left;text-wrap: wrap;border-top: none;border-right: none;border-bottom: none;overflow: auto;background: rgba(0, 0, 0, 0.05);'>\u003Cp style=\"padding-top: 8px;padding-bottom: 8px;font-size: 16px;color: black;line-height: 26px;\">Write a WeChat official account article introducing how to make LLMs smarter.\u003C/p>\u003C/blockquote>\u003Cblockquote data-tool=\"mdnice编辑器\" style='margin-top: 20px;margin-bottom: 20px;padding: 10px 10px 10px 20px;border-left-color: rgba(0, 0, 0, 0.4);color: rgb(106, 115, 125);font-size: 0.9em;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;letter-spacing: normal;text-align: left;text-wrap: wrap;border-top: none;border-right: none;border-bottom: none;overflow: auto;background-image: initial;background-position: initial;background-size: initial;background-repeat: initial;background-attachment: initial;background-origin: initial;background-clip: initial;'>\u003Cp style=\"padding-top: 8px;padding-bottom: 8px;font-size: 16px;color: black;line-height: 26px;\">To\u003C/p>\u003C/blockquote>\u003Cblockquote data-tool=\"mdnice编辑器\" style='margin-top: 20px;margin-bottom: 20px;padding: 10px 10px 10px 20px;border-left-color: rgba(0, 0, 0, 0.4);color: rgb(106, 115, 125);font-size: 0.9em;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;letter-spacing: normal;text-align: left;text-wrap: wrap;border-top: none;border-right: none;border-bottom: none;overflow: auto;background: rgba(0, 0, 0, 0.05);'>\u003Cp style=\"padding-top: 8px;padding-bottom: 8px;font-size: 16px;color: black;line-height: 26px;\">Senior WeChat Official Account operator\u003C/p>\u003C/blockquote>\u003Cblockquote data-tool=\"mdnice编辑器\" style='margin-top: 20px;margin-bottom: 20px;padding: 10px 10px 10px 20px;border-left-color: rgba(0, 0, 0, 0.4);color: rgb(106, 115, 125);font-size: 0.9em;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;letter-spacing: normal;text-align: left;text-wrap: wrap;border-top: none;border-right: none;border-bottom: none;overflow: auto;background: rgba(0, 0, 0, 0.05);'>\u003Cp style=\"padding-top: 8px;padding-bottom: 8px;font-size: 16px;color: black;line-height: 26px;\">\"System One, System Two\"\u003C/p>\u003C/blockquote>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>\u003Cstrong>Using Few-Shot combined with Chain-of-Thought (CoT)\u003C/strong>\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>Directly asking LLM \"What is 1364 multiplied by 2343?\" may result in a wrong answer.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003124\" data-ratio=\"0.5358024691358024\" data-s=\"300,640\" data-type=\"png\" data-w=\"405\" style=\"\" src=\"./assets/17423813087630.22455043880922276.png\">\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>However, we can improve this process by modifying the prompt, for example:\u003C/p>\u003Cpre data-tool=\"mdnice编辑器\" style=\"margin-top: 10px;margin-bottom: 10px;color: rgb(0, 0, 0);font-size: 16px;letter-spacing: normal;text-align: left;border-radius: 5px;box-shadow: rgba(0, 0, 0, 0.55) 0px 2px 10px;\">\u003Cspan style='margin-bottom: -7px;display: block;background: url(\"./assets/17423813113760.3203697312356175.svg\") 10px 10px / 40px no-repeat rgb(40, 44, 52);height: 30px;width: 558px;border-radius: 5px;'>\u003C/span>\u003Ccode style='padding: 15px 16px 16px;overflow-x: auto;color: rgb(171, 178, 191);display: -webkit-box;font-family: \"Operator Mono\", Consolas, Monaco, Menlo, monospace;font-size: 12px;background: rgb(40, 44, 52);border-radius: 5px;'>Then add up the results: Now, according to the above method, calculate what 1364 multiplied by 2343 equals.\u003C/code>\u003C/pre>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>This time the answer is correct:\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003125\" data-ratio=\"0.8214285714285714\" data-s=\"300,640\" data-type=\"png\" data-w=\"448\" style=\"\" src=\"./assets/17423813087870.012302420443337292.png\">\u003C/p>\u003Csection data-tool=\"mdnice编辑器\" style='margin-top: 20px;margin-bottom: 20px;padding: 10px 20px;color: rgb(0, 0, 0);font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;background: rgb(250, 250, 250);'>\u003Csection>\u003Cp style=\"padding-top: 8px;padding-bottom: 8px;line-height: 26px;\">\u003Cstrong>This process is similar to a decision-making flow. Making decisions based solely on one's own thoughts may not be sufficient; such a decision-making process can be used, similar to how LLMs improve the decision-making process by referencing CoT through Few-Shot.\u003C/strong>\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cstrong>\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003135\" data-ratio=\"0.41346153846153844\" data-s=\"300,640\" data-type=\"png\" data-w=\"728\" style=\"\" src=\"./assets/17423813087930.9037550616119279.png\">\u003C/strong>\u003C/p>\u003Cp>\u003Cbr>\u003C/p>\u003C/section>\u003C/section>\u003Ch2 data-tool=\"mdnice编辑器\" style='margin-top: 30px;margin-bottom: 15px;font-weight: bold;font-size: 22px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;letter-spacing: normal;text-align: left;text-wrap: wrap;'>2. Fine-Tuning\u003C/h2>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>I once listened to a podcast that mentioned if you enhance the imitation ability in one aspect during the fine-tuning process, the abilities in other areas might weaken. However, this method can make the LLM smarter in the direction we hope for.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003126\" data-ratio=\"0.7841031149301826\" data-s=\"300,640\" data-type=\"png\" data-w=\"931\" style=\"\" src=\"./assets/17423813088180.061453690638035186.png\">\u003Cspan style='font-family: mp-quote, -apple-system-font, BlinkMacSystemFont, \"Helvetica Neue\", \"PingFang SC\", \"Hiragino Sans GB\", \"Microsoft YaHei UI\", \"Microsoft YaHei\", Arial, sans-serif;font-size: var(--articleFontsize);letter-spacing: 0.034em;text-align: justify;'>\u003C/span>\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>It should be clear that Fine-Tuning is different from Few-Shot mentioned earlier. Fine-Tuning changes the model itself, whereas Few-Shot does not alter the model structure.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003127\" data-ratio=\"0.5731481481481482\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" style=\"\" src=\"./assets/17423813103700.2077887871782107.png\">\u003Cspan style='font-family: mp-quote, -apple-system-font, BlinkMacSystemFont, \"Helvetica Neue\", \"PingFang SC\", \"Hiragino Sans GB\", \"Microsoft YaHei UI\", \"Microsoft YaHei\", Arial, sans-serif;font-size: var(--articleFontsize);letter-spacing: 0.034em;text-align: justify;'>\u003C/span>\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>this changes his brain model compared to just giving examples.\u003C/p>\u003Csection data-tool=\"mdnice编辑器\" style='margin-top: 20px;margin-bottom: 20px;padding: 10px 20px;color: rgb(0, 0, 0);font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;background: rgb(250, 250, 250);'>\u003Csection>\u003Cp style=\"padding-top: 8px;padding-bottom: 8px;line-height: 26px;\">This is similar to using a decision-making model. You can guide and train the brain by choosing an appropriate direction based on your preferences. After getting used to rational decision-making, it may become less easy to make emotional decisions. Therefore, it's important to choose the most suitable method or the most appropriate one for a specific situation.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003136\" data-ratio=\"0.5681381957773513\" data-s=\"300,640\" data-type=\"png\" data-w=\"521\" style=\"\" src=\"./assets/17423813101810.5526932952192614.png\">\u003C/p>\u003Cp>\u003Cbr>\u003C/p>\u003C/section>\u003C/section>\u003Ch2 data-tool=\"mdnice编辑器\" style='margin-top: 30px;margin-bottom: 15px;font-weight: bold;font-size: 22px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;letter-spacing: normal;text-align: left;text-wrap: wrap;'>3. RAG（Retrieval Augmented Generation）\u003C/h2>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>RAG (Retrieval Augmented Generation, retrieval enhanced generation) is a technology where large language models (LLMs) retrieve relevant information from a large number of documents before answering questions or generating text, and then answer or generate text based on the retrieved information. This can improve the quality of the response rather than relying solely on the capabilities of the LLM itself. RAG technology allows developers to attach relevant knowledge bases without retraining the entire large model for each specific task, providing additional information input for the model and thus improving the accuracy of the responses. The workflow of RAG technology is shown in the figure below.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003128\" data-ratio=\"0.462037037037037\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" style=\"\" src=\"./assets/17423813106480.20199660196245062.png\">\u003Cspan style='font-family: mp-quote, -apple-system-font, BlinkMacSystemFont, \"Helvetica Neue\", \"PingFang SC\", \"Hiragino Sans GB\", \"Microsoft YaHei UI\", \"Microsoft YaHei\", Arial, sans-serif;font-size: var(--articleFontsize);letter-spacing: 0.034em;text-align: justify;'>\u003C/span>\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>In my view, Claude2 does a pretty good job with RAG.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003129\" data-ratio=\"0.799721835883171\" data-s=\"300,640\" data-type=\"png\" data-w=\"719\" style=\"\" src=\"./assets/17423813108880.9394252065572679.png\">\u003Cspan style='font-family: mp-quote, -apple-system-font, BlinkMacSystemFont, \"Helvetica Neue\", \"PingFang SC\", \"Hiragino Sans GB\", \"Microsoft YaHei UI\", \"Microsoft YaHei\", Arial, sans-serif;font-size: var(--articleFontsize);letter-spacing: 0.034em;text-align: justify;'>\u003C/span>\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>Some others that perform well include Perplexity, txyz, ChatGPT, chatpdf, etc.\u003C/p>\u003Csection data-tool=\"mdnice编辑器\" style='margin-top: 20px;margin-bottom: 20px;padding: 10px 20px;color: rgb(0, 0, 0);font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;background: rgb(250, 250, 250);'>\u003Csection>\u003Cp style=\"padding-top: 8px;padding-bottom: 8px;line-height: 26px;\">\u003Cstrong>This process is somewhat similar to how we make decisions: when we know we are not strong in a certain area, we can seek targeted consultations from professionals in that field. By leveraging their professional expertise, we can make wiser decisions. For example, in the case of the Thai Sleeping Beauty Cave rescue, data from different experts was RAGed.\u003C/strong>\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cstrong>\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003137\" data-ratio=\"0.5231481481481481\" data-s=\"300,640\" data-type=\"jpeg\" data-w=\"1080\" style=\"\" src=\"./assets/17423813112090.5945281866743941.jpeg\">\u003C/strong>\u003C/p>\u003Cp>\u003Cbr>\u003C/p>\u003C/section>\u003C/section>\u003Ch2 data-tool=\"mdnice编辑器\" style='margin-top: 30px;margin-bottom: 15px;font-weight: bold;font-size: 22px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;letter-spacing: normal;text-align: left;text-wrap: wrap;'>4. Function calls and tool usage.\u003C/h2>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>The GPT last launch announced the Function Call feature, which greatly expanded the boundaries of what LLMs can do.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003130\" data-ratio=\"0.7961053837342497\" data-s=\"300,640\" data-type=\"png\" data-w=\"873\" style=\"\" src=\"./assets/17423813099470.9934976950011385.png\">\u003Cspan style='font-family: mp-quote, -apple-system-font, BlinkMacSystemFont, \"Helvetica Neue\", \"PingFang SC\", \"Hiragino Sans GB\", \"Microsoft YaHei UI\", \"Microsoft YaHei\", Arial, sans-serif;font-size: var(--articleFontsize);letter-spacing: 0.034em;text-align: justify;'>\u003C/span>\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>The latest version of GPT has become smarter, mainly by incorporating the previous code interpreter as an analytical function, allowing it to call Python when handling complex problems.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003131\" data-ratio=\"0.6296296296296297\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" style=\"\" src=\"./assets/17423813098480.2801766020106917.png\">\u003C/p>\u003Cp>Before this feature appeared, the earliest related paper I saw was \"Program-Aided Language (PAL) Models,\" where such models use large language models to understand questions and leverage Python for calculations.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003142\" data-ratio=\"0.7308809293320426\" data-s=\"300,640\" data-type=\"png\" data-w=\"1033\" style=\"\" src=\"./assets/17423813110220.6330538225019415.png\">\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>PAL was later also integrated into the open-source framework Langchain.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003143\" data-ratio=\"1.0016\" data-s=\"300,640\" data-type=\"png\" data-w=\"625\" style=\"\" src=\"./assets/17423813105430.28585707460543364.png\">\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>Langchain itself also developed the Math chain feature.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003144\" data-ratio=\"0.9665071770334929\" data-s=\"300,640\" data-type=\"png\" data-w=\"627\" style=\"\" src=\"./assets/17423813106480.055937940622575955.png\">\u003Cspan style='font-family: mp-quote, -apple-system-font, BlinkMacSystemFont, \"Helvetica Neue\", \"PingFang SC\", \"Hiragino Sans GB\", \"Microsoft YaHei UI\", \"Microsoft YaHei\", Arial, sans-serif;font-size: var(--articleFontsize);letter-spacing: 0.034em;text-align: justify;'>\u003C/span>\u003C/p>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>Of course, OpenAI also provides various plugins, such as a calculator.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003134\" data-ratio=\"0.34074074074074073\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" style=\"\" src=\"./assets/17423813101330.08983346031793094.png\">\u003Cspan style='font-family: mp-quote, -apple-system-font, BlinkMacSystemFont, \"Helvetica Neue\", \"PingFang SC\", \"Hiragino Sans GB\", \"Microsoft YaHei UI\", \"Microsoft YaHei\", Arial, sans-serif;font-size: var(--articleFontsize);letter-spacing: 0.034em;text-align: justify;'>\u003C/span>\u003C/p>\u003Csection data-tool=\"mdnice编辑器\" style='margin-top: 20px;margin-bottom: 20px;padding: 10px 20px;color: rgb(0, 0, 0);font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;background: rgb(250, 250, 250);'>\u003Csection>\u003Cp style=\"padding-top: 8px;padding-bottom: 8px;line-height: 26px;\">\u003Cstrong>This process of calling functions and using tools is similar to the methods we use when making decisions: we may be aware of our limited abilities, but we can use various tools to assist in decision-making. By using these tools, we can solve problems more effectively and make better decisions.\u003C/strong>\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cstrong>\u003Cimg class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100003139\" data-ratio=\"0.5625\" data-s=\"300,640\" data-type=\"png\" data-w=\"1024\" style=\"\" src=\"./assets/17423813111720.8636108112483154.png\">\u003C/strong>\u003C/p>\u003Cp>\u003Cbr>\u003C/p>\u003C/section>\u003C/section>\u003Ch2 data-tool=\"mdnice编辑器\" style='margin-top: 30px;margin-bottom: 15px;font-weight: bold;font-size: 22px;text-wrap: wrap;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;letter-spacing: normal;text-align: left;'>Final Remarks\u003C/h2>\u003Cp data-tool=\"mdnice编辑器\" style='margin-bottom: 0px;padding-top: 8px;padding-bottom: 8px;color: black;font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, \"PingFang SC\", Cambria, Cochin, Georgia, Times, \"Times New Roman\", serif;font-size: 16px;letter-spacing: normal;text-align: left;text-wrap: wrap;line-height: 26px;'>Just as we seek information, consult experts, or use various tools when making decisions in daily life, the development of large language models (LLMs) also reflects this diversified and comprehensive thinking pattern. Whether by asking better questions, fine-tuning models, using retrieval-augmented generation (RAG) technology, or learning to effectively call functions and use tools, these strategies all emphasize the importance of flexibly utilizing resources and technology when facing complex problems. I hope that the colleagues in our company can learn to use LLMs better, making LLMs smarter; and also\u003C/p>\u003Cp style=\"display: none;\">\u003Cmp-style-type data-value=\"3\">\u003C/mp-style-type>\u003C/p>\u003C/div>",[257,266,274,282,291,299,307,315],{"id":258,"title_md5":259,"publish_date":260,"author_md5":261,"is_original":4,"collection":262,"summary_md5":263,"cover_url":264,"cover_url_1_1":265},331,"ba36b00a94e7c9b866a7e67db56079a5","2024-02-04","bc27fa490c4d0d525bac812fc0793534","#AI Game #AI Agent","f2b376fba0153a860d58d5c3f77788dd","article_res/cover/a06fb659ab32b0ddcf3ebb6c330fc35f.jpeg","article_res/cover/df5aa34d411e2e9d1a3115cb816fde82.jpeg",{"id":267,"title_md5":268,"publish_date":269,"author_md5":270,"is_original":4,"collection":5,"summary_md5":271,"cover_url":272,"cover_url_1_1":273},592,"e88cffc6b542cdbfe19e2e5c77d69deb","2022-03-25","8b3607d0f4181a3cb6ffdccf7185f09b","40bae0f2b1ea67933a0d3cf04c3d2725","article_res/cover/4034319b0cd2a3db4eb33ac1f1e3d4cd.jpeg","article_res/cover/e97b8ef59089ce9ee82d2aec858f3335.jpeg",{"id":275,"title_md5":276,"publish_date":277,"author_md5":261,"is_original":23,"collection":278,"summary_md5":279,"cover_url":280,"cover_url_1_1":281},174,"bda05a4c84c53ba3437c1f5363ae96eb","2024-09-18","#Elon Musk #All-In Summit 2024 #Nvidia","fab8efe359fa38ced92bfb896520874e","article_res/cover/f244d398c0ee0a7772cb9116263afbd1.jpeg","article_res/cover/802ee578f1e63ea0d375f95898f43d0d.jpeg",{"id":283,"title_md5":284,"publish_date":285,"author_md5":286,"is_original":4,"collection":287,"summary_md5":288,"cover_url":289,"cover_url_1_1":290},512,"a7e99f6e3921bf69fc994dae723fc0bc","2022-06-25","8936f53b23e9dfe2965d9e2ec23a4779","#Philosophy","b118be0853ceab732ba10ecccf078ef7","article_res/cover/f4500b06d6383effc25eb83a7ad6b035.jpeg","article_res/cover/ecf655f8012c89487d9fd130ac13c9f0.jpeg",{"id":292,"title_md5":293,"publish_date":294,"author_md5":261,"is_original":23,"collection":295,"summary_md5":296,"cover_url":297,"cover_url_1_1":298},119,"526d0f374ee04f855666789603e8bdf8","2024-11-25","#AI Image Generator","f3521bc722ed915ad6e32225f2baf710","article_res/cover/621440bd2cd3230cd8481c4ed44378f1.jpeg","article_res/cover/177d7b243e807ca843f41ac82be538e1.jpeg",{"id":300,"title_md5":301,"publish_date":302,"author_md5":261,"is_original":23,"collection":303,"summary_md5":304,"cover_url":305,"cover_url_1_1":306},284,"df06faf78334ae24ae423ef6625b6109","2024-05-04","#AI Index Report 2024","a53842d54fcd985f443a4a695022cb73","article_res/cover/cba905132168c996108de5af21120030.jpeg","article_res/cover/056d14bc0f7a1f12715fdd4ef9f261dc.jpeg",{"id":308,"title_md5":309,"publish_date":310,"author_md5":261,"is_original":4,"collection":311,"summary_md5":312,"cover_url":313,"cover_url_1_1":314},408,"9bc772a5c7613f1af36a3b13036a6c17","2023-09-21","#AI Agents #LLM #AI Agent","55fbf3dbedcb325d132b0e8d22e03d77","article_res/cover/897f57a35c9bd3bb8e8c738dae940108.jpeg","article_res/cover/f83dfe7f16aef354ff7e6e1372afd074.jpeg",{"id":71,"title_md5":316,"publish_date":72,"author_md5":317,"is_original":4,"collection":73,"summary_md5":318,"cover_url":74,"cover_url_1_1":75},"30803ee2e2b6b9bc08b755ddf51d4c2b","56e9d1e4a4e518466ea53b126afb91ef","e324c110d08236428712015cdb1d9ffe",{"related":320,"small":360},[321,328,336,344,352],{"id":322,"publish_date":323,"is_original":4,"collection":5,"cover_url":324,"cover_url_1_1":325,"title":326,"summary":327,"author":28},319,"2024-03-01","article_res/cover/5239bb311dd89be0b89b37cece07216a.jpeg","article_res/cover/e83780b53298fd487334b869e8193983.jpeg","Directory tree knowledge - Globe Explorer: AI Search Engine + Wikipedia","Exploring knowledge has never been easier or more effective!",{"id":329,"publish_date":330,"is_original":4,"collection":5,"cover_url":331,"cover_url_1_1":332,"title":333,"summary":334,"author":335},539,"2022-05-17","article_res/cover/c7827fdb8a24ad69e8258305dc11c5b7.jpeg","article_res/cover/e7a8bfe89e48f53c8a6669306014c930.jpeg","\"The Burning of Books and Burying of Scholars\"","The imperial cause turned to naught as bamboo and silk were consumed by smoke, and the land of rivers and passes locked in vain the abode of the First Qin Emperor.  \nThe ashes from the pits were not yet cold when turmoil arose east of the mountains; it turns out that Liu Bang and Xiang Yu never read books.","Excerpt",{"id":337,"publish_date":338,"is_original":23,"collection":339,"cover_url":340,"cover_url_1_1":341,"title":342,"summary":343,"author":28},224,"2024-07-09","#AI Avatar","article_res/cover/670fbb56c51f582e4b359a0b5c9eefd0.jpeg","article_res/cover/c9a84424a4c803f0e2565e847e9f36ac.jpeg","Kuaishou's LivePortrait - A Video-driven Avatar Animation Framework","LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control",{"id":345,"publish_date":346,"is_original":23,"collection":347,"cover_url":348,"cover_url_1_1":349,"title":350,"summary":351,"author":28},369,"2023-12-16","#AI Image Generator #AI Animation","article_res/cover/1d85e2d37ef038479ef70d74da3a2dcc.jpeg","article_res/cover/2693cb77c01973daf1e2737a95d3ef40.jpeg","Fast image generation - Latent Consistency Models (LCMs)","LCMs to overcome the slow iterative sampling process of LDMs (e.g., SD), enabling fast inference with minimal steps.",{"id":353,"publish_date":354,"is_original":23,"collection":355,"cover_url":356,"cover_url_1_1":357,"title":358,"summary":359,"author":28},63,"2025-01-22","#Microsoft #Research #Nature #AI Science #chemistry","article_res/cover/d2f6a78ac00110002e05dbd868952d51.jpeg","article_res/cover/bc00a61c35d090b9734d647c8c1830ba.jpeg","Microsoft's MatterGen - A material discovery tool based on generative AI","MatterGen: A new paradigm of materials design with generative AI",[361,367,373],{"title":10,"list":362},[363,364,365,366],{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"title":222,"list":368},[369,370,371,372],{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"id":227,"publish_date":228,"is_original":23,"collection":229,"cover_url":230,"cover_url_1_1":231,"title":232,"summary":233,"author":28},{"id":235,"publish_date":236,"is_original":23,"collection":73,"cover_url":237,"cover_url_1_1":238,"title":239,"summary":240,"author":28},{"title":242,"list":374},[],[8,9,10],[8,12,13,14,9,10,15,16,17,18],["Reactive",245],1754646414936]