Techniques & Methods

Training with deliberately challenging inputs to make AI models more robust and accurate.

The process of making AI behavior and outputs conform to human intentions and ethical standards.

The process of checking each important claim in an answer against permitted sources and flagging anything unsupported.

A mechanism that allows AI models to weigh the importance of different pieces of information.

A technique that helps AI models focus on relevant parts of their input data.

A relevant, trusted, permitted, and current source you can rely on when checking a claim.

A statistical approach that predicts future behavior from past outcomes in time-series data.

A method for training artificial neural networks by adjusting weights in response to error rates.

A reasoning method that starts with a goal and works backward to identify a path to the solution.

A strategy that balances exploring new choices with exploiting options known to produce rewards.

A search algorithm that efficiently identifies the most likely sequences of outcomes in a model.

A prompting strategy that encourages AI to break complex problems into more manageable steps.

The output an AI produces in response to an input or prompt, completing a sentence or thought.

An NLP task that determines which words or phrases refer to the same entity in a text.

A technique that expands training data by adding modified copies or synthetic examples.

The practice of examining large datasets to uncover new information and hidden patterns.

Guidelines that dictate how a language model translates its internal representations to output.

Analyzing the grammatical structure of a sentence to understand relationships between words.

A method where AI model training is spread across multiple computers or servers.

The process of labeling text with information about entities, enhancing data structure.

Identifying and classifying named entities in text into predefined categories.

Quantitative measures used to assess the performance and effectiveness of AI models.

Creating summaries by extracting key sentences or fragments directly from the source text.

Identifying and isolating useful information from data to improve model training and performance.

The ability of a model to learn and generalize from a very small number of examples.

The process of adjusting a pre-trained model to perform well on a specific task or dataset.

The capability to precisely adjust the output or behavior of an AI model based on specific criteria.

A logical reasoning method that starts with known facts and applies rules to reach new conclusions.

The process of producing new content, such as text or images, based on learned patterns and data.

Optimization algorithms that make the locally optimal choice at each step to find a global optimum.

When AI generates information that is not grounded in reality, often because of limitations in its training data.

Problem-solving approaches that rely on practical methods or shortcuts to reach a solution.

The phase in which a trained model makes predictions or decisions from new, unseen data.

The process of automatically extracting structured information from unstructured data such as text.

The probability that two events happen at the same time in a probabilistic model.

The way AI systems model, store, and retrieve knowledge to solve complex tasks.

The process of adding metadata about linguistic features to text so it can be analyzed.

A technique for fine-tuning large models in a memory-efficient and computationally efficient way.

A mathematical framework for modeling decision-making in situations with random outcomes.

A training technique in which some words in the input are hidden and the model predicts them.

Training one AI model on multiple tasks at the same time by leveraging what those tasks share.

The process of identifying and classifying key entities in text into predefined categories.

A reinforcement learning approach that learns optimal actions from a fixed dataset without further interaction with the environment.

Learning techniques in which a model uses one example or a small number of examples, respectively.

A model's ability to learn new information or a new task from a single example or a few examples.

A training approach in which a model updates continuously as new data arrives.

A technique that discourages repetitive or overly similar responses from generative AI models.

The process of labeling each word in a text with its corresponding part of speech.

The initial phase in which a model learns from a large, general dataset before task-specific training.

Text given to an AI model to elicit a specific type of response or output.

The practice of crafting prompts that communicate effectively with AI models and elicit desired responses.

A technique that uses specially crafted inputs to influence or manipulate an AI system's behavior.

A reinforcement learning algorithm that balances exploration with exploitation during policy learning.

A request for information or an action sent to a database, search engine, or AI model.

Techniques that help prevent overfitting by penalizing model complexity during training.

A training approach that refines models using feedback from human evaluators.

A measure of how well an AI system's responses meet standards for relevance, coherence, and accuracy.

An approach that combines relevant retrieved information with generative models to produce informed responses.

Observed patterns showing that AI model performance improves predictably as model size increases.

A mechanism that lets a model weigh the importance of different parts of its input relative to one another.

The process of adding semantic metadata to content so AI systems can understand and process it more easily.

A measure of how closely two pieces of text are related in meaning, often used in NLP tasks.

The process of producing an ordered series of items, such as words in generated text, from patterns an AI model has learned.

When information that was once correct no longer applies because a date, product, or policy has changed.

The process of improving a model on specific tasks through further training with labeled data.

Internal instructions that guide an AI model's behavior and influence how it processes and responds to input.

The task of assigning text to predefined categories, as in spam detection and sentiment analysis.

A statistical method for discovering abstract topics in a collection of documents to support content organization and discovery.

The process of teaching a machine learning model to make predictions or decisions, typically with a large dataset.

A machine learning approach that applies knowledge gained from one problem to a different but related problem.

A generative AI technique that produces multiple outputs and selects the best one by a chosen set of criteria.

The process of evaluating a model on separate data that was not used in training to estimate its accuracy.

Different phrasings that express the same intent or meaning, which matters when modeling natural language variation.

The encoding of words or phrases as numerical vectors so AI models can compare them and perform mathematical operations.

An NLP technique that represents words as vectors in a high-dimensional space to capture semantic similarity.

A model's ability to perform tasks it was not explicitly trained to do, demonstrating generalization.