Glossary

The process of ensuring that AI systems behave in ways that are consistent with human values and intentions.

The degree to which a model’s decision-making process can be understood by humans.

Designing prompts to test or exploit vulnerabilities in AI models.

An experimental, educational framework for exploring ergonomic, lightweight multi-agent orchestration.

A technique that allows models to focus on different parts of the input when generating output.

Chain-of-thought prompting is a strategy that encourages an AI model to articulate its reasoning process step-by-step. This method often leads to more accurate and transparent decision-making.

Techniques to align AI models with specific values or principles through careful prompt design.

Using prompts to limit the model’s output to specific formats or content types.

The maximum amount of text a model can process in a single prompt.

The ability of a model to apply knowledge from one type of prompt to a different but related task.

AI agents that can adapt their behavior and instructions based on context and previous interactions.

Dense vector representations of words, sentences, or other data types in a high-dimensional space.

AI systems designed to provide clear explanations for their outputs or decisions.

The process of selecting, modifying, or creating new features from raw data to improve the performance of machine learning models.

A machine learning technique that trains algorithms across multiple decentralized devices or servers holding local data samples.

Few-shot prompting is a method that involves providing a small number of examples to guide an AI model's performance on a task.

The process of further training a pre-trained model on a specific dataset to adapt it to a particular task or domain.

Enabling AI models to call specific functions to perform tasks or retrieve information.

A framework where two neural networks (a generator and a discriminator) compete against each other to create realistic data.

An optimization algorithm used to minimize the cost function in machine learning by iteratively updating the model parameters.

When an AI model generates false or nonsensical information that it presents as factual.

The model’s ability to adapt to new tasks based on information provided within the prompt.

Fine-tuning language models on datasets focused on instruction-following tasks.

A machine learning technique that aims to transfer the learnings of a large pre-trained model (the "teacher model") to a smaller "student model."