Chain-of-thought prompting - Chain-of-thought prompting is a prompting approach that uses intermediate explanations or worked solution steps to help a language model produce an answer.
Few-shot learning - Few-shot learning is the challenge of adapting to a task or recognizing new classes from only a small number of labeled examples.
Few-shot prompting - Few-shot prompting means including a small number of example inputs and desired outputs in a prompt before asking a model to handle a new input.
Generalized Few-shot Learning - Generalized few-shot learning is a classification setting in which a model must recognize both previously learned base classes and new classes introduced...
Human-in-the-loop - Human-in-the-loop describes an AI workflow in which people provide input, judgment, correction or approval at defined points that influence the system's...
Incremental Few-shot Learning - Incremental few-shot learning is learning new categories or capabilities in successive stages from small labeled datasets while retaining useful performance...
Instruction tuning - Instruction tuning is additional training that teaches a language model to respond to tasks expressed as instructions.
Least-to-most prompting - Least-to-most prompting is a language-model prompting strategy that first decomposes a problem into simpler subproblems and then solves them in sequence...
Multimodal Prompt-based Language Model - A multimodal prompt-based language model generates language in response to prompts containing more than one kind of information, such as text combined with...
One-shot prompting - One-shot prompting gives a language model one demonstration of the desired task before asking it to handle a new input.
Prompt - A prompt is input supplied to a generative AI model to guide the output it produces.
Prompt-based learning - Prompt-based learning uses prompts to guide a pretrained model toward a task or desired behavior.
Prompt chaining - Prompt chaining connects multiple model calls so that an earlier call's output becomes part of a later call's input.
Prompt engineering - Prompt engineering is the deliberate design and testing of instructions given to an AI model.
Prompt set - A prompt set is a collection of prompts used to evaluate a language model or an AI application.
Prompt tuning - Prompt tuning is a parameter-efficient adaptation method that learns continuous prompt vectors while keeping the underlying pretrained model fixed.
Role prompting - Role prompting asks a generative AI model to respond from a specified role or persona, such as a patient tutor, editor or interviewer.
Soft prompt tuning - Soft prompt tuning adapts a pretrained model by learning a small set of continuous prompt vectors while keeping the base model's weights fixed.
System prompt - A system prompt is an application-level instruction supplied to a language model to establish its role, task, behavior or response conventions.
Tree-of-thought prompting - Tree-of-thought prompting organizes language-model problem solving as a search over alternative intermediate steps.
Zero-shot - Zero-shot describes using a model on a target task or category without supplying task-specific demonstrations or labeled training examples under the stated...
Zero-shot learning - Zero-shot learning is a learning setting in which a model recognizes target classes without labeled training examples of those classes, using transferable...
Zero-shot prompting - Zero-shot prompting asks a model to perform a task without including a completed example of that task in the prompt.
Zero-shot TTS - Zero-shot TTS is text-to-speech synthesis that generates speech for a target speaker without separately fine-tuning the synthesis model on that speaker.