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route","\u002Fthank-you",{},"data\u002Fshared\u002Furls","P27xKEauu8D-8sfyr0wR4giF0teFSaCuAQ8kgcICQdI",{"id":590,"title":591,"author":592,"authorPhoto":593,"authorPhotoAlt":594,"authorSlug":595,"body":596,"buttonText":1025,"buttonUrl":594,"category":1026,"date":1027,"description":1028,"draft":1029,"editorsPick":1029,"extension":1030,"featured":1029,"image":1031,"imageAlt":594,"meta":1032,"navigation":20,"ogImage":594,"ogImageAlt":594,"path":1033,"seo":1034,"stem":1035,"tags":594,"__hash__":1036},"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":597,"value":598,"toc":1001},"minimark",[599,611,614,619,622,629,644,650,672,681,685,688,693,696,701,704,707,710,718,722,725,728,740,743,747,750,758,761,765,768,776,779,782,790,799,803,812,816,819,830,833,837,840,842,847,850,854,857,865,869,872,876,879,890,894,897,912,917,920,931,935,940,949,954,965,969,973,982,991,994],[600,601,602,603,610],"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 ",[604,605,609],"a",{"href":606,"rel":607},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fbest-developer-productivity-tools",[608],"nofollow","developer tools",".",[600,612,613],{},"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.",[615,616,618],"h2",{"id":617},"what-are-large-language-models","What are large language models?",[600,620,621],{},"Large language models (LLMs) are advanced AI systems designed to understand and generate human-like text.",[600,623,624],{},[625,626],"img",{"alt":627,"src":628},"","https:\u002F\u002Fstorage.googleapis.com\u002Fpieces-marketing-website\u002Fimages\u002Fblog\u002Flarge-language-models\u002Fimg-001.png",[600,630,631,632,637,638,643],{},"These models leverage vast datasets and billions (or even trillions) of ",[604,633,636],{"href":634,"rel":635},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fllm-parameters",[608],"parameters"," to perform a variety of ",[604,639,642],{"href":640,"rel":641},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fnlp-chatbots-an-overview-of-natural-language-processing-in-chatbot-technology",[608],"natural language processing"," (NLP) tasks, such as text generation, summarization, translation, and more.",[600,645,646],{},[647,648,649],"strong",{},"What are the key characteristics of LLMs?",[651,652,653,660,666],"ul",{},[654,655,656,659],"li",{},[647,657,658],{},"Scale:"," LLMs are \"large\" because they have billions of parameters, which are the internal weights that help the model learn language patterns.",[654,661,662,665],{},[647,663,664],{},"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.",[654,667,668,671],{},[647,669,670],{},"Versatility:"," Unlike traditional AI models designed for single tasks, LLMs can adapt to a wide range of tasks without requiring task-specific training.",[600,673,674,675,680],{},"For example, ChatGPT, built on GPT (Generative Pre-trained Transformer) architecture, can perform tasks like answering questions, providing recommendations, ",[604,676,679],{"href":677,"rel":678},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fhow-to-write-code-with-chatgpt",[608],"writing code",", and even generating creative text, all powered by LLM technology.",[615,682,684],{"id":683},"how-do-large-language-models-work","How do large language models work?",[600,686,687],{},"At their core, LLMs predict the next word or token in a sequence, enabling them to generate coherent and contextually relevant text.",[600,689,690],{},[625,691],{"alt":627,"src":692},"https:\u002F\u002Fstorage.googleapis.com\u002Fpieces-marketing-website\u002Fimages\u002Fblog\u002Flarge-language-models\u002Fimg-002.png",[600,694,695],{},"This process involves several steps:",[697,698,700],"h3",{"id":699},"data-collection","Data collection",[600,702,703],{},"LLMs are trained on diverse datasets that include web pages, books, articles, and even code repositories.",[600,705,706],{},"This diversity ensures the model can handle various language styles, topics, and domains.",[600,708,709],{},"For instance:",[651,711,712,715],{},[654,713,714],{},"Training datasets may consist of hundreds of billions of words or tokens.",[654,716,717],{},"The inclusion of different forms of text (e.g., dialogues, academic papers, and code) equips LLMs to perform a wide range of tasks.",[697,719,721],{"id":720},"tokenization","Tokenization",[600,723,724],{},"Before processing text, LLMs break it into smaller units called tokens. Tokens can be words, subwords, or even individual characters.",[600,726,727],{},"For example:",[651,729,730],{},[654,731,732,733,736,610],{},"The sentence \"Large language models are powerful\" might be tokenized as:",[734,735],"br",{},[737,738,739],"span",{},"\"Large\", \"language\", \"models\", \"are\", \"powerful\"",[600,741,742],{},"Tokenization helps the model process text efficiently, allowing it to understand language structure.",[697,744,746],{"id":745},"training-objectives","Training objectives",[600,748,749],{},"LLMs are trained using two primary objectives:",[651,751,752,755],{},[654,753,754],{},"Masked language modeling (MLM): The model predicts missing words in a sentence (e.g., \"The cat sat on the ___\").",[654,756,757],{},"Causal language modeling (CLM): The model predicts the next word in a sequence (e.g., \"The cat sat on the\").",[600,759,760],{},"Think of it like a supercharged version of autocomplete, where the model learns to generate text by predicting what comes next based on context.",[697,762,764],{"id":763},"fine-tuning","Fine-tuning",[600,766,767],{},"After pretraining, LLMs can be fine-tuned for specific use cases:",[651,769,770,773],{},[654,771,772],{},"Instruction tuning: Helps the model follow user instructions more effectively.",[654,774,775],{},"Reinforcement learning from human feedback (RLHF): Uses feedback from humans to improve the quality, safety, and relevance of the model's outputs.",[600,777,778],{},"For example, fine-tuning allows LLMs to generate more accurate code suggestions or provide domain-specific answers in healthcare or law.",[600,780,781],{},"Important note that there are also differences between cloud llms and local llms.",[600,783,784,789],{},[604,785,788],{"href":786,"rel":787},"https:\u002F\u002Fpieces.app\u002Fblog\u002Flocal-large-language-models-lllms-and-copilot-integrations",[608],"Local llms"," are gaining certain traction because they’re believed to be “more secure”.",[600,791,792,793,798],{},"Though some devs go with ",[604,794,797],{"href":795,"rel":796},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fbest-llm-for-coding-cloud-vs-local",[608],"cloud llms"," that are as popular in the dev community.",[615,800,802],{"id":801},"key-architecture-and-training-methods","Key architecture and training methods",[600,804,805,806,811],{},"Modern LLMs are built on transformer architecture, a revolutionary ",[604,807,810],{"href":808,"rel":809},"https:\u002F\u002Fmedium.com\u002F@lmpo\u002Fa-brief-history-of-lmms-from-transformers-2017-to-deepseek-r1-2025-dae75dd3f59a",[608],"framework introduced in 2017",". Transformers allow models to process large amounts of text efficiently while maintaining contextual understanding.",[697,813,815],{"id":814},"transformer-fundamentals","Transformer fundamentals",[600,817,818],{},"The transformer architecture consists of:",[651,820,821,824,827],{},[654,822,823],{},"Encoders: Process input text.",[654,825,826],{},"Decoders: Generate output text.",[654,828,829],{},"Self-attention mechanisms: Enable the model to focus on relevant parts of the input when interpreting meaning.",[600,831,832],{},"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.",[697,834,836],{"id":835},"attention-mechanisms","Attention mechanisms",[600,838,839],{},"Self-attention helps LLMs focus on important parts of a sentence.",[600,841,709],{},[651,843,844],{},[654,845,846],{},"In the sentence \"The cat sat on the mat because it was soft\", the model uses attention to link \"it\" to \"mat\".",[600,848,849],{},"Think of attention as a highlighting tool that helps the model prioritize the most relevant words.",[697,851,853],{"id":852},"scaling-laws","Scaling laws",[600,855,856],{},"Research shows that larger models trained on more data perform better.",[600,858,859,860,610],{},"However, this improvement follows a power law, meaning that while performance increases, the cost and resources required ",[604,861,864],{"href":862,"rel":863},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fsmall-language-models-outshine-large-language-models-enterprise-users",[608],"grow exponentially",[615,866,868],{"id":867},"llm-use-cases-for-developers","LLM use cases for developers",[600,870,871],{},"Large language models are incredibly powerful tools for developers, offering practical applications that enhance productivity:",[697,873,875],{"id":874},"workflow-automation","Workflow automation",[600,877,878],{},"LLMs automate repetitive tasks like:",[651,880,881,884,887],{},[654,882,883],{},"Generating boilerplate code.",[654,885,886],{},"Formatting and refactoring code.",[654,888,889],{},"Creating unit tests for new functions.",[697,891,893],{"id":892},"code-assistance","Code assistance",[600,895,896],{},"LLMs provide real-time code suggestions and explanations, helping developers write better code faster.",[600,898,899,900,905,906,911],{},"Tools like Pieces, alongside with other free and paid ",[604,901,904],{"href":902,"rel":903},"https:\u002F\u002Fpieces.app\u002Fblog\u002Ftop-10-ai-tools-for-developers",[608],"ai tools for developers",",  integrate LLMs to streamline workflows by offering ",[604,907,910],{"href":908,"rel":909},"https:\u002F\u002Fpieces.app\u002Ffeatures\u002Fcopilot\u002Fcontext-awareness",[608],"context-aware recommendations"," within coding environments.",[913,914,916],"h4",{"id":915},"_3-documentation-generation","3. Documentation generation",[600,918,919],{},"LLMs can generate high-quality documentation, including:",[651,921,922,925,928],{},[654,923,924],{},"API documentation.",[654,926,927],{},"Inline comments.",[654,929,930],{},"Technical writing for complex systems.",[615,932,934],{"id":933},"benefits-and-challenges-of-llms","Benefits and challenges of LLMs",[600,936,937],{},[647,938,939],{},"Benefits",[941,942,943,946],"ol",{},[654,944,945],{},"Efficiency: LLMs save time by automating mundane tasks and retaining context across sessions.",[654,947,948],{},"Flexibility: They can handle a wide range of tasks from text generation to code analysis.",[600,950,951],{},[647,952,953],{},"Challenges",[941,955,956,959,962],{},[654,957,958],{},"Bias and ethics: LLMs can reflect biases in their training data, leading to biased outputs.",[654,960,961],{},"Resource constraints: Training and running LLMs require significant computational power.",[654,963,964],{},"Hallucination: LLMs sometimes generate plausible-sounding but incorrect information.",[615,966,968],{"id":967},"future-outlook-for-large-models","Future outlook for large models",[697,970,972],{"id":971},"ready-for-whats-next-in-ai","Ready for What’s Next in AI?",[600,974,975,976,981],{},"The future of large models is evolving fast. Whether it's ",[604,977,980],{"href":978,"rel":979},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fmultimodal-ai-bridging-the-gap-between-human-and-machine-understanding",[608],"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.",[600,983,984,985,990],{},"👉 Curious about how small language models (SLMs) differ from large ones? Read ",[604,986,989],{"href":987,"rel":988},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fllm-vs-slm",[608],"this article"," to dive deeper into the distinctions and discover which is right for your use case.",[600,992,993],{},"Stay ahead.",[600,995,996,997,610],{},"Explore the tools shaping tomorrow’s AI – ",[604,998,1000],{"href":206,"rel":999},[608],"today",{"title":627,"searchDepth":1002,"depth":1002,"links":1003},2,[1004,1005,1012,1017,1021,1022],{"id":617,"depth":1002,"text":618},{"id":683,"depth":1002,"text":684,"children":1006},[1007,1009,1010,1011],{"id":699,"depth":1008,"text":700},3,{"id":720,"depth":1008,"text":721},{"id":745,"depth":1008,"text":746},{"id":763,"depth":1008,"text":764},{"id":801,"depth":1002,"text":802,"children":1013},[1014,1015,1016],{"id":814,"depth":1008,"text":815},{"id":835,"depth":1008,"text":836},{"id":852,"depth":1008,"text":853},{"id":867,"depth":1002,"text":868,"children":1018},[1019,1020],{"id":874,"depth":1008,"text":875},{"id":892,"depth":1008,"text":893},{"id":933,"depth":1002,"text":934},{"id":967,"depth":1002,"text":968,"children":1023},[1024],{"id":971,"depth":1008,"text":972},"Get started","AI & LLM","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":591,"description":1028},"blog\u002Flarge-language-models","xl8M5YNHpm6bF-E59jMT0FlxjYZkM2maiRpcVqK4FwE",{"id":1038,"title":592,"body":1039,"description":627,"draft":1029,"extension":1030,"meta":1043,"navigation":20,"path":1044,"photo":1045,"photoAlt":594,"seo":1046,"stem":1047,"__hash__":1048},"authors\u002Fauthors\u002Fthe-pieces-team.md",{"type":597,"value":1040,"toc":1041},[],{"title":627,"searchDepth":1002,"depth":1002,"links":1042},[],{},"\u002Fauthors\u002Fthe-pieces-team","https:\u002F\u002Fstorage.googleapis.com\u002Fpieces-marketing-website\u002Fimages\u002Fauthors\u002Fthe-pieces-team.png",{"title":592,"description":627},"authors\u002Fthe-pieces-team","_2O_QA8d8RjXlBp0ck4PWDG6iA74zW1kN0R0gkn2Lls"]