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error route","\u002Fauth\u002Ferror",{},"data\u002Fshared\u002Furls","NLkVE1lIUws518K-1QsSB6T-GvlXYIzcRL1HDhqarGQ",{"id":664,"title":665,"author":666,"authorPhoto":667,"authorPhotoAlt":668,"authorSlug":669,"body":670,"buttonText":1099,"buttonUrl":668,"category":1100,"date":1101,"description":1102,"draft":1103,"editorsPick":1103,"extension":1104,"featured":1103,"image":1105,"imageAlt":668,"meta":1106,"navigation":32,"ogImage":668,"ogImageAlt":668,"path":1107,"seo":1108,"stem":1109,"tags":668,"__hash__":1110},"blog\u002Fblog\u002Flarge-language-models.md","What are large language models? A technical breakdown","The Pieces Team","https:\u002F\u002Fstorage.googleapis.com\u002Fpieces-marketing-website\u002Fimages\u002Fblog\u002Fdevops-platforms\u002Fauthor.png",null,"the-pieces-team",{"type":671,"value":672,"toc":1075},"minimark",[673,685,688,693,696,703,718,724,746,755,759,762,767,770,775,778,781,784,792,796,799,802,814,817,821,824,832,835,839,842,850,853,856,864,873,877,886,890,893,904,907,911,914,916,921,924,928,931,939,943,946,950,953,964,968,971,986,991,994,1005,1009,1014,1023,1028,1039,1043,1047,1056,1065,1068],[674,675,676,677,684],"p",{},"Large language models (LLMs) have become a cornerstone of modern artificial intelligence (AI), powering everything from conversational AI systems like ChatGPT to advanced search engines and ",[678,679,683],"a",{"href":680,"rel":681},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fbest-developer-productivity-tools",[682],"nofollow","developer tools",".",[674,686,687],{},"If you're wondering \"what is an LLM?\" or want to explore how these models work, their architecture, and their real-world applications, this technical breakdown will provide the answers.",[689,690,692],"h2",{"id":691},"what-are-large-language-models","What are large language models?",[674,694,695],{},"Large language models (LLMs) are advanced AI systems designed to understand and generate human-like text.",[674,697,698],{},[699,700],"img",{"alt":701,"src":702},"","https:\u002F\u002Fstorage.googleapis.com\u002Fpieces-marketing-website\u002Fimages\u002Fblog\u002Flarge-language-models\u002Fimg-001.png",[674,704,705,706,711,712,717],{},"These models leverage vast datasets and billions (or even trillions) of ",[678,707,710],{"href":708,"rel":709},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fllm-parameters",[682],"parameters"," to perform a variety of ",[678,713,716],{"href":714,"rel":715},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fnlp-chatbots-an-overview-of-natural-language-processing-in-chatbot-technology",[682],"natural language processing"," (NLP) tasks, such as text generation, summarization, translation, and more.",[674,719,720],{},[721,722,723],"strong",{},"What are the key characteristics of LLMs?",[725,726,727,734,740],"ul",{},[728,729,730,733],"li",{},[721,731,732],{},"Scale:"," LLMs are \"large\" because they have billions of parameters, which are the internal weights that help the model learn language patterns.",[728,735,736,739],{},[721,737,738],{},"Self-supervised learning:"," Instead of relying on labeled datasets, LLMs are trained on massive, unstructured text corpora using self-supervised learning. This allows them to learn patterns, grammar, facts, and context from raw text.",[728,741,742,745],{},[721,743,744],{},"Versatility:"," Unlike traditional AI models designed for single tasks, LLMs can adapt to a wide range of tasks without requiring task-specific training.",[674,747,748,749,754],{},"For example, ChatGPT, built on GPT (Generative Pre-trained Transformer) architecture, can perform tasks like answering questions, providing recommendations, ",[678,750,753],{"href":751,"rel":752},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fhow-to-write-code-with-chatgpt",[682],"writing code",", and even generating creative text, all powered by LLM technology.",[689,756,758],{"id":757},"how-do-large-language-models-work","How do large language models work?",[674,760,761],{},"At their core, LLMs predict the next word or token in a sequence, enabling them to generate coherent and contextually relevant text.",[674,763,764],{},[699,765],{"alt":701,"src":766},"https:\u002F\u002Fstorage.googleapis.com\u002Fpieces-marketing-website\u002Fimages\u002Fblog\u002Flarge-language-models\u002Fimg-002.png",[674,768,769],{},"This process involves several steps:",[771,772,774],"h3",{"id":773},"data-collection","Data collection",[674,776,777],{},"LLMs are trained on diverse datasets that include web pages, books, articles, and even code repositories.",[674,779,780],{},"This diversity ensures the model can handle various language styles, topics, and domains.",[674,782,783],{},"For instance:",[725,785,786,789],{},[728,787,788],{},"Training datasets may consist of hundreds of billions of words or tokens.",[728,790,791],{},"The inclusion of different forms of text (e.g., dialogues, academic papers, and code) equips LLMs to perform a wide range of tasks.",[771,793,795],{"id":794},"tokenization","Tokenization",[674,797,798],{},"Before processing text, LLMs break it into smaller units called tokens. Tokens can be words, subwords, or even individual characters.",[674,800,801],{},"For example:",[725,803,804],{},[728,805,806,807,810,684],{},"The sentence \"Large language models are powerful\" might be tokenized as:",[808,809],"br",{},[811,812,813],"span",{},"\"Large\", \"language\", \"models\", \"are\", \"powerful\"",[674,815,816],{},"Tokenization helps the model process text efficiently, allowing it to understand language structure.",[771,818,820],{"id":819},"training-objectives","Training objectives",[674,822,823],{},"LLMs are trained using two primary objectives:",[725,825,826,829],{},[728,827,828],{},"Masked language modeling (MLM): The model predicts missing words in a sentence (e.g., \"The cat sat on the ___\").",[728,830,831],{},"Causal language modeling (CLM): The model predicts the next word in a sequence (e.g., \"The cat sat on the\").",[674,833,834],{},"Think of it like a supercharged version of autocomplete, where the model learns to generate text by predicting what comes next based on context.",[771,836,838],{"id":837},"fine-tuning","Fine-tuning",[674,840,841],{},"After pretraining, LLMs can be fine-tuned for specific use cases:",[725,843,844,847],{},[728,845,846],{},"Instruction tuning: Helps the model follow user instructions more effectively.",[728,848,849],{},"Reinforcement learning from human feedback (RLHF): Uses feedback from humans to improve the quality, safety, and relevance of the model's outputs.",[674,851,852],{},"For example, fine-tuning allows LLMs to generate more accurate code suggestions or provide domain-specific answers in healthcare or law.",[674,854,855],{},"Important note that there are also differences between cloud llms and local llms.",[674,857,858,863],{},[678,859,862],{"href":860,"rel":861},"https:\u002F\u002Fpieces.app\u002Fblog\u002Flocal-large-language-models-lllms-and-copilot-integrations",[682],"Local llms"," are gaining certain traction because they’re believed to be “more secure”.",[674,865,866,867,872],{},"Though some devs go with ",[678,868,871],{"href":869,"rel":870},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fbest-llm-for-coding-cloud-vs-local",[682],"cloud llms"," that are as popular in the dev community.",[689,874,876],{"id":875},"key-architecture-and-training-methods","Key architecture and training methods",[674,878,879,880,885],{},"Modern LLMs are built on transformer architecture, a revolutionary ",[678,881,884],{"href":882,"rel":883},"https:\u002F\u002Fmedium.com\u002F@lmpo\u002Fa-brief-history-of-lmms-from-transformers-2017-to-deepseek-r1-2025-dae75dd3f59a",[682],"framework introduced in 2017",". Transformers allow models to process large amounts of text efficiently while maintaining contextual understanding.",[771,887,889],{"id":888},"transformer-fundamentals","Transformer fundamentals",[674,891,892],{},"The transformer architecture consists of:",[725,894,895,898,901],{},[728,896,897],{},"Encoders: Process input text.",[728,899,900],{},"Decoders: Generate output text.",[728,902,903],{},"Self-attention mechanisms: Enable the model to focus on relevant parts of the input when interpreting meaning.",[674,905,906],{},"This architecture allows LLMs to process entire sentences (or even paragraphs) in parallel, rather than word by word, making them faster and more contextually aware.",[771,908,910],{"id":909},"attention-mechanisms","Attention mechanisms",[674,912,913],{},"Self-attention helps LLMs focus on important parts of a sentence.",[674,915,783],{},[725,917,918],{},[728,919,920],{},"In the sentence \"The cat sat on the mat because it was soft\", the model uses attention to link \"it\" to \"mat\".",[674,922,923],{},"Think of attention as a highlighting tool that helps the model prioritize the most relevant words.",[771,925,927],{"id":926},"scaling-laws","Scaling laws",[674,929,930],{},"Research shows that larger models trained on more data perform better.",[674,932,933,934,684],{},"However, this improvement follows a power law, meaning that while performance increases, the cost and resources required ",[678,935,938],{"href":936,"rel":937},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fsmall-language-models-outshine-large-language-models-enterprise-users",[682],"grow exponentially",[689,940,942],{"id":941},"llm-use-cases-for-developers","LLM use cases for developers",[674,944,945],{},"Large language models are incredibly powerful tools for developers, offering practical applications that enhance productivity:",[771,947,949],{"id":948},"workflow-automation","Workflow automation",[674,951,952],{},"LLMs automate repetitive tasks like:",[725,954,955,958,961],{},[728,956,957],{},"Generating boilerplate code.",[728,959,960],{},"Formatting and refactoring code.",[728,962,963],{},"Creating unit tests for new functions.",[771,965,967],{"id":966},"code-assistance","Code assistance",[674,969,970],{},"LLMs provide real-time code suggestions and explanations, helping developers write better code faster.",[674,972,973,974,979,980,985],{},"Tools like Pieces, alongside with other free and paid ",[678,975,978],{"href":976,"rel":977},"https:\u002F\u002Fpieces.app\u002Fblog\u002Ftop-10-ai-tools-for-developers",[682],"ai tools for developers",",  integrate LLMs to streamline workflows by offering ",[678,981,984],{"href":982,"rel":983},"https:\u002F\u002Fpieces.app\u002Ffeatures\u002Fcopilot\u002Fcontext-awareness",[682],"context-aware recommendations"," within coding environments.",[987,988,990],"h4",{"id":989},"_3-documentation-generation","3. Documentation generation",[674,992,993],{},"LLMs can generate high-quality documentation, including:",[725,995,996,999,1002],{},[728,997,998],{},"API documentation.",[728,1000,1001],{},"Inline comments.",[728,1003,1004],{},"Technical writing for complex systems.",[689,1006,1008],{"id":1007},"benefits-and-challenges-of-llms","Benefits and challenges of LLMs",[674,1010,1011],{},[721,1012,1013],{},"Benefits",[1015,1016,1017,1020],"ol",{},[728,1018,1019],{},"Efficiency: LLMs save time by automating mundane tasks and retaining context across sessions.",[728,1021,1022],{},"Flexibility: They can handle a wide range of tasks from text generation to code analysis.",[674,1024,1025],{},[721,1026,1027],{},"Challenges",[1015,1029,1030,1033,1036],{},[728,1031,1032],{},"Bias and ethics: LLMs can reflect biases in their training data, leading to biased outputs.",[728,1034,1035],{},"Resource constraints: Training and running LLMs require significant computational power.",[728,1037,1038],{},"Hallucination: LLMs sometimes generate plausible-sounding but incorrect information.",[689,1040,1042],{"id":1041},"future-outlook-for-large-models","Future outlook for large models",[771,1044,1046],{"id":1045},"ready-for-whats-next-in-ai","Ready for What’s Next in AI?",[674,1048,1049,1050,1055],{},"The future of large models is evolving fast. Whether it's ",[678,1051,1054],{"href":1052,"rel":1053},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fmultimodal-ai-bridging-the-gap-between-human-and-machine-understanding",[682],"multimodal capabilities"," blending text, images, and code, leaner models optimized for your device, or safer, smarter outputs, we're entering a new era of AI innovation.",[674,1057,1058,1059,1064],{},"👉 Curious about how small language models (SLMs) differ from large ones? Read ",[678,1060,1063],{"href":1061,"rel":1062},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fllm-vs-slm",[682],"this article"," to dive deeper into the distinctions and discover which is right for your use case.",[674,1066,1067],{},"Stay ahead.",[674,1069,1070,1071,684],{},"Explore the tools shaping tomorrow’s AI – ",[678,1072,1074],{"href":259,"rel":1073},[682],"today",{"title":701,"searchDepth":1076,"depth":1076,"links":1077},2,[1078,1079,1086,1091,1095,1096],{"id":691,"depth":1076,"text":692},{"id":757,"depth":1076,"text":758,"children":1080},[1081,1083,1084,1085],{"id":773,"depth":1082,"text":774},3,{"id":794,"depth":1082,"text":795},{"id":819,"depth":1082,"text":820},{"id":837,"depth":1082,"text":838},{"id":875,"depth":1076,"text":876,"children":1087},[1088,1089,1090],{"id":888,"depth":1082,"text":889},{"id":909,"depth":1082,"text":910},{"id":926,"depth":1082,"text":927},{"id":941,"depth":1076,"text":942,"children":1092},[1093,1094],{"id":948,"depth":1082,"text":949},{"id":966,"depth":1082,"text":967},{"id":1007,"depth":1076,"text":1008},{"id":1041,"depth":1076,"text":1042,"children":1097},[1098],{"id":1045,"depth":1082,"text":1046},"Get started","AI & LLMs","2025-06-13T00:00:00.000Z","Explore the technical foundations of large language models (LLMs), including how they work, their architecture, training methods, and real-world applications",false,"md","https:\u002F\u002Fstorage.googleapis.com\u002Fpieces-marketing-website\u002Fimages\u002Fblog\u002Flarge-language-models\u002Fhero.png",{},"\u002Fblog\u002Flarge-language-models",{"title":665,"description":1102},"blog\u002Flarge-language-models","Cm8Y7L11nugss2eSmNM5F8YPB10-Mhk--B_4pBTwFdI",{"id":1112,"title":666,"body":1113,"description":701,"draft":1103,"extension":1104,"meta":1117,"navigation":32,"path":1118,"photo":1119,"photoAlt":668,"seo":1120,"stem":1121,"__hash__":1122},"authors\u002Fauthors\u002Fthe-pieces-team.md",{"type":671,"value":1114,"toc":1115},[],{"title":701,"searchDepth":1076,"depth":1076,"links":1116},[],{},"\u002Fauthors\u002Fthe-pieces-team","https:\u002F\u002Fstorage.googleapis.com\u002Fpieces-marketing-website\u002Fimages\u002Fauthors\u002Fthe-pieces-team.png",{"title":666,"description":701},"authors\u002Fthe-pieces-team","_2O_QA8d8RjXlBp0ck4PWDG6iA74zW1kN0R0gkn2Lls",{"left":1124,"top":1124,"width":1125,"height":1125,"rotate":1124,"vFlip":1103,"hFlip":1103,"body":1126},0,24,"\u003Cpath fill=\"currentColor\" d=\"m7.825 13l4.9 4.9q.3.3.288.7t-.313.7q-.3.275-.7.288t-.7-.288l-6.6-6.6q-.15-.15-.213-.325T4.426 12t.063-.375t.212-.325l6.6-6.6q.275-.275.688-.275t.712.275q.3.3.3.713t-.3.712L7.825 11H19q.425 0 .713.288T20 12t-.288.713T19 13z\"\u002F>"]