Testing Large Language Models (LLMs) and related AI systems has become increasingly complex as the technology evolves. This glossary provides up-to-date definitions of key terms you’ll encounter in LLM testing, including new concepts, methodologies, and regulatory considerations.
Term | Explanation |
Agentic AI | AI systems or agents capable of planning, making decisions, and executing multi-step tasks autonomously, often interacting with external tools or APIs. |
Adversarial Testing | A testing approach that intentionally crafts inputs to expose vulnerabilities, such as prompt injection or model manipulation. |
Alignment | The process of ensuring an LLM’s outputs are consistent with human values, organizational goals, and ethical standards. |
Bias Detection | Methods and tools used to identify and measure unwanted biases in LLM outputs, such as gender, racial, or cultural bias. |
Continuous Monitoring | Ongoing evaluation of LLM performance, safety, and fairness after deployment to detect issues like model drift or emerging risks. |
Data Privacy | Practices and safeguards to ensure that sensitive or personal data used in LLM training and testing is protected and compliant with regulations (e.g., GDPR, EU AI Act). |
Explainability | The degree to which the reasoning behind an LLM’s output can be understood and communicated to users or stakeholders. |
Fairness Testing | Assessing whether an LLM’s outputs are equitable across different groups and do not perpetuate harmful stereotypes or discrimination. |
Hallucination | When an LLM generates outputs that are factually incorrect, fabricated, or not grounded in its training data. |
Human-in-the-Loop (HITL) | A testing or deployment setup where human reviewers oversee, validate, or intervene in LLM outputs, especially for critical or high-risk tasks. |
Jailbreaking | Techniques used to bypass an LLM’s built-in safety or ethical constraints, often to elicit restricted or harmful outputs. |
Large Language Model (LLM) | A neural network-based AI model trained on vast datasets to understand and generate human language, typically with billions of parameters. |
Model Drift | The phenomenon where an LLM’s performance degrades over time due to changes in data, user behavior, or external factors. |
Multimodal Model | An AI model capable of processing and generating content across multiple data types (e.g., text, images, audio, video) simultaneously. |
N-shot Learning | A method where an LLM is prompted with N examples to perform a task, improving its ability to generalize from limited data. |
Prompt Engineering | The practice of designing and refining prompts to elicit desired behaviors or outputs from an LLM. |
Prompt Injection | A security vulnerability where malicious or cleverly crafted prompts manipulate an LLM into producing unintended or harmful outputs. |
Responsible AI | A set of practices and principles ensuring AI systems are ethical, transparent, fair, and compliant with regulations (e.g., EU AI Act, NIST AI RMF). |
Small Language Model (SLM) | A more compact language model, typically with fewer parameters than an LLM, optimized for efficiency and on-device applications. |
Synthetic Data | Artificially generated data used for training or testing LLMs, often to enhance privacy or address data scarcity. |
Zero-shot Learning | A method where an LLM performs a task without any prior examples, relying solely on its general knowledge. |