LLM?

LLM stands for Large Language Model, which is a type of AI model. Here's a breakdown of what LLMs are and how they're used, according to the provided sources:
* LLMs are prediction engines that take sequential text as input and predict the subsequent token based on their training data^[2].
* They are tuned to follow instructions and have been trained on vast datasets, enabling them to comprehend prompts and generate responses^[2].
* LLMs can be used for understanding and generation tasks like text summarization, information extraction, question answering, text classification, language or code translation, code generation, and code documentation or reasoning^[2].
* LLMs can understand complex inputs, engage in reasoning and planning, use tools reliably, and recover from errors^[1].
* They dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks^[1].
* When using reasoning, to come up with the final answer, there’s likely one single correct answer. Therefore the temperature should always set to 0^[2].
* They’re becoming increasingly capable of handling complex, multi-step tasks^[3]. Advances in reasoning, multimodality, and tool use have unlocked a new category of LLM-powered systems known as agents^[3].
* AI agents: Are engineered to achieve specific objectives by perceiving their environment and strategically acting upon it using the tools at their disposal^[4]. The fundamental principle of an agent lies in its synthesis of reasoning, logic, and access to external information^[4].
* Key component of AI agents: The model, which pertains to the language model (LM) that functions as the central decision-making unit, employing instruction-based reasoning and logical frameworks^[4].
* A key enabler of AI applications where systems can reason through ambiguity, take action across tools, and handle multi-step tasks with a high degree of autonomy^[3].
* Can be used in systems where LLMs and tools are orchestrated through predefined code paths^[1].
* Can be used in systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks^[1].