AI uses its own working vocabulary, and a lot of it sounds more mysterious than it really is. Terms like “models,” “tokens,” “reasoning steps,” or “context windows” simply describe how these systems process information, break down language, and build answers. Some of this is specific to XPlain-R as we are in unknown territory in aspects of this emerging technology.
Getting comfortable with this terminology makes it easier to understand what an AI is actually doing — and what it isn’t doing — when it collaborates with you. If you know the basics, you can read AI outputs with more precision, spot limits faster, and get better work from the tools.
XPlain Terminology
This glossary provides terminology for the XPLAIN-R ecosystem and other AI related terms.
Analytical Hierarchy Process (AHP)
A structured technique for organising and analysing decisions using mathematical and psychological principles.
Assurance Loop
A verification cycle ensuring conformance, integrity, and correctness via structures such as Evidence Gate and Trace Review.
Avatar Context
Identity framing used for tailored interaction in safety-focused primers.
Bench Pack
A curated benchmark set for testing and comparing primers.
Cognitive Frame
A reusable pattern or lens for analysing a request, such as stakes or reversibility.
Comparability
The property allowing outputs from different AI platforms or time periods to be evaluated against shared benchmarks.
Construct
A modular building block defining a functional part of the AI’s reasoning or behaviour.
Decision / Output
The primer block where the final recommendation and rationale are formed.
Deeper Rigor (AHP/ANP)
Enhanced analysis for high-stakes contexts using multi-criteria methods.
Directive Statement
A fixed set of non-negotiable rules defining how the AI must behave across all tasks.
Evidence Gate
A structural block validating factual support, sources, and citations.
Exception Handling
Logic by which the AI escalates ambiguity or boundary conditions for human review.
Explainability
The capability to provide transparent, interpretable reasoning behind each decision or output.
Failure Modes
A diagnostic block anticipating potential weaknesses or risks.
Flashpoint Retrospective
A mechanism for reassessing earlier predictions based on later evidence.
Historical Trace
A compressed lineage of past Romers used for continuity in long-running projects.
Hybrid Primer Protocol
Rules governing how static instructions and adaptive learning interlock.
Human Validation Gate
A mandatory approval point for high-stakes decisions.
Insight Capture
Distilled interpretations or patterns feeding back into the Meta-Primer.
Interpretability Layer
The mechanism translating user intent and primer rules into structured reasoning.
Interpretive Boundaries
Rules defining limits on AI interpretation such as no invention of standards or pseudo-citations.
Interpretive Drift
A detectable shift in reasoning approach due to accumulated learning or ambiguity.
Interpretive Grid
A frame for triaging user requests based on stakes, scope, or dependencies.
Interpretive Safeguard
A rule forcing escalation to safety layers for sensitive or risky topics.
ISO Standards
Reference standards such as ISO 9001, 14001, 31000, 27001, 37301, and others relevant for GRC and AI governance.
Learning Capture
Systematic extraction of new heuristics or insights after each Romer.
Learning Loop
The cycle where Romer output leads to primer update and subsequent refined reasoning.
Learning Model
The structure by which an AI adapts over time, including heuristic and interpretive learning.
Market Note
A concise interpretive signal capturing persistent structural consequences.
Meta-Primer
A higher-order construct defining how primers are generated, evaluated, and evolved.
Meta-Primer Registry
An index tracking active and archived Meta-Primer versions.
Method Statement
A structured human-authored procedure for how something is to be done.
NIST Frameworks
Reference frameworks such as the NIST AI-RMF and Cybersecurity Framework.
Operational Directive
A narrower directive governing rules for a specific project or task.
Pause Point
A deliberate stop requiring human validation during reasoning.
Primer
A structured reasoning framework guiding decision-making and analysis.
Primer Conformance
A measure of how strictly a primer schema was followed.
Primer Safety Layer
A hidden block ensuring protective responses to sensitive topics.
Process
A structured series of steps evolved through recursive refinement.
Provenance Hash
A cryptographic identifier ensuring integrity of Romer traces.
Recursive Execution
Iterative refinement of actions and reasoning based on prior steps.
Right-Sized Rigor
Mechanism adjusting analytical depth proportionally to stakes.
Rightness Check
A lightweight self-check performed before output finalisation.
Romer
A recorded execution of a primer, capturing reasoning and insights.
Romer Classifications
Types of Romers such as exploratory, diagnostic, decision, assurance, or retrospective.
Romer Trace
The detailed log of a primer execution, used for assurance and auditing.
Run Signature
A human-readable identifier for referencing a Romer run.
Scoring Model
A structured rubric used in SCEAS scoring or primer evaluation.
Self-Assurance
The AI’s internal capability to evaluate its own outputs.
Self-Checking
AI behaviour where each execution step is monitored and verified.
Self-Evolving
The AI’s ability to modify its interpretation and application over time.
Signals in the Quiet
A block capturing subtle indicators relevant to forecasting.
Starter Method Statement
The initial structured instructions guiding AI interpretation.
STAR-XAI
A framework emphasising Structure, Transparency, Auditability, and Reasoning in explainable AI.
Subject Matter Expert (SME)
A domain expert defining tasks and clarifying exceptions.
Swing Node
A system actor occupying a pivot point between competing blocs or networks.
TIOC
A temporal reasoning model of ideological overreach and correction cycles.
Topical Primer
A domain-specific primer used for specialised analytic tasks.
UN/CEFACT Standards
Trade and data exchange standards such as CCL and the Buy-Ship-Pay model.
‘What to Think About’ Frame
An orientation block used at the start of monthly analytical reports.
XAI
Explainable AI methods centred on transparent decision-making.
General AI Terminology
This glossary explains commonly used AI and machine learning terms in clear, human language. Use the search box to quickly find a concept.
API (Application Programming Interface)
A standard way for software systems to talk to each other. For AI, an API lets apps send prompts and receive model outputs programmatically.
Alignment
The degree to which an AI system behaves in line with human goals, values, and safety expectations, rather than just optimising its raw objective.
Autoregressive Model
A model that generates one token at a time, each step based on all previous tokens (e.g. GPT-style language models).
Benchmark
A standardised test or dataset used to compare the performance of different AI models on the same task.
Chain-of-Thought
An AI reasoning style where the model outputs intermediate steps or explanations, not just a final answer.
Chatbot
An AI system designed to converse with users in natural language, typically via text or voice.
Context Window
The maximum amount of text (prompt + history + tools, etc.) an AI model can consider at once when generating a response.
Embedding
A numeric vector representation of text or other data that captures semantic meaning, used for search, clustering, and similarity.
Few-Shot Learning
Teaching the model how to do a task by showing a few examples directly in the prompt, instead of retraining the model.
Fine-Tuning
Training an existing model further on a specific dataset so it specialises in a domain, tone, or task.
Foundation Model
A large, general-purpose model trained on broad data (e.g. GPT) that can be adapted or prompted to handle many downstream tasks.
Generative AI
AI systems that create new content — text, images, code, audio, or video — rather than just classifying or ranking existing data.
GPU (Graphics Processing Unit)
A type of processor well-suited to the parallel computations needed for training and running large AI models.
Guardrails
Controls, policies, and system prompts that constrain what an AI is allowed to do or say, especially for safety and compliance.
Hallucination
When an AI confidently produces incorrect or fabricated information that looks plausible but is not grounded in reality or its sources.
Inference
The process of running a trained model to produce outputs (e.g. generating a completion) as opposed to training or fine-tuning it.
Latent Space
The internal high-dimensional representation where the model encodes patterns and relationships between concepts during training and inference.
LLM (Large Language Model)
A neural network trained on large amounts of text to predict and generate language, often used as the core of modern chatbots.
Loss Function
A mathematical formula that measures how wrong a model’s predictions are during training, guiding how its parameters are updated.
Model
The mathematical object (network + parameters) that takes inputs and produces outputs, learned from data rather than hand-coded.
Multi-Modal Model
A model that can work with more than one type of input or output, such as text, images, audio, or combinations of these.
Parameters
The numeric weights inside a model that are adjusted during training and determine how it behaves; often counted in billions.
Prompt
The input given to an AI model — instructions, examples, and context that guide the model’s next response.
Prompt Engineering
The practice of crafting and structuring prompts to get more accurate, reliable, or useful outputs from AI models.
RAG (Retrieval-Augmented Generation)
A pattern where the AI first retrieves relevant documents or data, then uses them as context to generate a grounded response.
Reinforcement Learning
A training approach where an agent learns by trial and error, receiving rewards or penalties based on its actions.
RLHF (Reinforcement Learning from Human Feedback)
A method where humans rate model outputs, and those ratings are used to further train the model to produce more aligned responses.
Safety (AI Safety)
The field focused on preventing harmful, unethical, or unintended behaviours from AI systems, especially as they grow more capable.
System Prompt
A hidden or higher-level instruction that sets baseline behaviour for a model (tone, rules, constraints) before user prompts are applied.
Temperature
A setting that controls how “creative” or random the model is. Lower values make it more focused and deterministic; higher values make it more varied.
Token
A unit of text the model processes, usually a word or sub-word chunk. Context window limits are measured in tokens.
Training Data
The dataset used to teach a model during training; its scope and quality strongly influence what the model knows and how it behaves.
Transformer
The neural network architecture behind most modern language models, based on attention mechanisms rather than older recurrent structures.
Zero-Shot Learning
When a model performs a task it has not seen explicit examples of in the prompt, relying on its general training and instructions alone.