From e89482b4594bb0f309b5693fab72e75c88055084 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Agust=C3=ADn=20Fern=C3=A1ndez?= <45650558+Dasher83@users.noreply.github.com> Date: Tue, 25 Nov 2025 06:06:51 -0300 Subject: [PATCH] Add video link on how LLMs work (#9287) * Add video link on how LLMs work Added a video link explaining how large language models work. * Update src/data/roadmaps/prompt-engineering/content/llms-and-how-they-work@74JxgfJ_1qmVNZ_QRp9Ne.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Javier Canales <56018501+jcanalesluna@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../content/llms-and-how-they-work@74JxgfJ_1qmVNZ_QRp9Ne.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/data/roadmaps/prompt-engineering/content/llms-and-how-they-work@74JxgfJ_1qmVNZ_QRp9Ne.md b/src/data/roadmaps/prompt-engineering/content/llms-and-how-they-work@74JxgfJ_1qmVNZ_QRp9Ne.md index 5c306d74a..4bdebf506 100644 --- a/src/data/roadmaps/prompt-engineering/content/llms-and-how-they-work@74JxgfJ_1qmVNZ_QRp9Ne.md +++ b/src/data/roadmaps/prompt-engineering/content/llms-and-how-they-work@74JxgfJ_1qmVNZ_QRp9Ne.md @@ -1,3 +1,5 @@ # LLMs and How They Work -LLMs function as sophisticated prediction engines that process text sequentially, predicting the next token based on relationships between previous tokens and patterns from training data. They don't predict single tokens directly but generate probability distributions over possible next tokens, which are then sampled using parameters like temperature and top-K. The model repeatedly adds predicted tokens to the sequence, building responses iteratively. This token-by-token prediction process, combined with massive training datasets, enables LLMs to generate coherent, contextually relevant text across diverse applications and domains. \ No newline at end of file +LLMs function as sophisticated prediction engines that process text sequentially, predicting the next token based on relationships between previous tokens and patterns from training data. They don't predict single tokens directly but generate probability distributions over possible next tokens, which are then sampled using parameters like temperature and top-K. The model repeatedly adds predicted tokens to the sequence, building responses iteratively. This token-by-token prediction process, combined with massive training datasets, enables LLMs to generate coherent, contextually relevant text across diverse applications and domains. + +- [@video@How Large Language Models Work](https://youtu.be/5sLYAQS9sWQ)