Can you explain common terminology such as what is an LLM, what is agentic AI, context memory, hallucinations, etc?

A Large Language Model (LLM) is a type of AI system capable of generating responses in a conversation, where each output is produced based on a sequence of preceding prompts and responses [1]. LLMs form the foundation for more advanced applications, such as AI agents, and are central to modern AI tools that process and generate human-like text [2]. Agentic AI refers to AI agents built on LLMs that can autonomously or semi-autonomously use tools, execute functions, or interact with external environments like file systems or APIs to perform tasks [6][10]. These agents often involve workflows where LLMs interleave planning, tool calls, and execution, but they can face challenges like forgetting earlier instructions or rushing outputs [1][4]. Hallucinations in LLMs occur when models produce unreliable or fabricated outputs, a problem being addressed in regulated industries through techniques like output constraints to improve reliability [3]. Context memory relates to how LLMs maintain conversation history or long-term session data, but it can lead to issues like context bloat in multi-session agent deployments, increasing costs and inefficiencies [7]; LLMs may also "forget" prior instructions, similar to attention deficits, disrupting agentic workflows [4]. The sources provide limited details on other terms like sycophancy, where LLMs overly agree with users, potentially biasing information [12].