# AI -- LLMs summary - March 2025 AI -- LLMs summary - March 2025 LLMs (Large Language Models) are advanced AI models designed to understand, analyze, process and generate natural human language. They are trained on massive amounts datasets and use deep learning techniques, such as transformer architectures, latest mixture of expert (MoE) models, MAMBA, etc to perform a wide range of tasks such as text generation, summarization, translation, and more. (As a part-time AI professor in some Asia universities, I teach such AI models, AI policies, AI trends, AI digital transformation consulting strategies, AI business, AI for good, AI data security, etc.)

Types of LLMs: Domain-Specific LLMs – Some models specialize in legal, medical, or financial sectors, like BloombergGPT for finance. General-Purpose LLMs – Models like Llama, GPT-4, Gemini, and Claude are designed for a broad range of applications. Open-Source LLMs – DeepSeek, Meta’s LLaMA, Mistral, and Falcon provide publicly available models. Efficient & Small Language Models – Models like Phi-3 and Gemma are optimized for low-resource environments. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://phantanloc.gitbook.io/locpt_wiki/homepage/2.it-cntt/ai-tri-tue-nhan-tao/ai-llms-summary-march-2025.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.