Concept Description AGI Artificial General Intelligence (AGI), the point at which AI matches or exceeds the intelligence of humans. Generative AI AI systems that create new content rather than just analyzing existing data. Foundation Models Large pre-trained models that serve as the base for various applications. Architecture Structural design of the model. Most modern LLMs use Transformer architectures with attention mechanisms. Attention Mechanisms Components allowing models to weigh importance of different words when generating text. Tokens Basic units LLMs process; can be words, parts of words, or characters. Tokenization The process of breaking text into tokens. Parameters Learnable weights in the neural network that determine model capabilities. More parameters (measured in billions) generally mean more knowledge and abilities. Context Window Maximum amount of text (measured in tokens) an LLM can consider at once. .safetensors Secure file format for storing model weights that prevents arbitrary code execution during loading. Completion/Response Text generated by the LLM in response to a prompt. Temperature Setting that controls randomness in responses鈥攈igher values produce more creative outputs. Prompt Input text given to an LLM to elicit a response. Prompt Engineering Skill of crafting effective prompts to get desired results from LLMs. Few-shot Learning Providing examples within a prompt to guide the model toward specific response formats. Instruction Tuning Training models to follow specific instructions rather than just predicting next words. Hallucination Hallucination in Large Language Models (LLMs) refers to when the model generates false, misleading, or non-factual information that sounds plausible but is incorrect. Embeddings Vector representations of words/text that capture semantic meaning and relationships. RAG (Retrieval-Augmented Generation) Enhancing LLM responses by retrieving relevant information from external sources. Training The process of teaching an AI model by feeding it data and adjusting its parameters. Inference Process of generating text from the model (as opposed to training). Fine-tuning Process of adapting pre-trained models to specific tasks using additional training data. RLHF (Reinforcement Learning from Human Feedback) Training technique to align LLMs with human preferences and improve safety. Epoch The number of times a model training process looked through a full data set of images. E.g. The 5th Epoch of a Checkpoint model looked five times through the same data set of images. float16 Half Precision, 16-bit float32 Full Precision, 32-bit