AI Encyclopedia & Dictionary
A structured, citation-backed reference for AI concepts, systems, and practices—built for decision-makers, builders, and teams who need clarity without hype.
Entries
35
Categories
4
Last refresh
Mar 2026
Foundational encyclopedia entries
Start with the concepts that shape AI strategy, governance, and real-world deployment.
Generative AI (GenAI)
A category of artificial intelligence models that can produce new text, images, code, or audio based on patterns learned from existing data.
AI Agents & Agentic Workflows
Autonomous or semi-autonomous AI systems designed to achieve specific goals by planning, reasoning, and using tools to take actions in digital environments.
AI Governance & Ethics
The frameworks, policies, and practices organizations use to manage the risks and ethical implications of developing and deploying AI systems.
Artificial Intelligence (AI)
Computer systems designed to perform tasks that typically require human intelligence, such as learning, reasoning, perception, or decision-making.
MLOps
Machine Learning Operations (MLOps) is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently.
Deep Learning
A subset of machine learning based on artificial neural networks with multiple layers.
Natural Language Processing (NLP)
A branch of artificial intelligence focused on enabling computers to understand, interpret, and generate human language.
Foundation Models
Large AI models trained on broad, general datasets at massive scale, designed to be adapted for a wide range of downstream tasks.
Reinforcement Learning from Human Feedback (RLHF)
A training technique that uses human preference ratings to fine-tune AI models to produce more helpful, accurate, and safe responses.
Computer Vision
A field of AI that enables machines to interpret and understand visual information from images and video.
Multimodal AI
AI systems that can process and generate content across multiple modalities—text, images, audio, and video—within a single model.
AI Workflow Automation
The use of AI models, agents, and integrations to automate multi-step business processes that previously required human judgment.
Apply what you learn
We help teams go from definitions to deployed workflows—safely and fast.
Built for clarity
Each entry is reviewed for accuracy and relevance, with citations to authoritative sources. We highlight what is known, what is still evolving, and how it impacts business outcomes.
Evidence-based sourcing
Every definition includes citations so teams can verify and explore deeper.
Human-centered explanations
Plain-language summaries help non-technical stakeholders act confidently.
Global perspective
We align definitions with international standards and major research bodies.
Operational focus
Coverage emphasizes practical application, governance, and AI readiness.
Knowledge paths
Follow curated paths that connect definitions, systems, and decision points.
Building Reliable AI Applications
Understand the components required to move a model into production.
AI Security & Trust
Mitigate risks when adopting generative models.
AI Dictionary (A–Z)
Quick-reference definitions with search and filters.
C
Chain-of-Thought Prompting
A prompting technique that instructs an AI model to reason step-by-step before arriving at a final answer, significantly improving accuracy on complex tasks.
Context Window
The maximum amount of text (measured in tokens) that an AI model can process at one time.
F
Fine-Tuning (PEFT, LoRA)
The process of taking a pre-trained model and training it further on a smaller, specialized dataset to adapt it to specific tasks or domains.
Function Calling (Tool Use)
A capability that allows LLMs to invoke structured external tools or APIs mid-conversation, enabling reliable real-world actions and grounded data retrieval.
M
Machine Learning (ML)
A field of AI where systems learn patterns from data to make predictions or decisions without being explicitly programmed for every rule.
Mixture of Experts (MoE)
An AI architecture that replaces a single dense neural network with multiple specialized sub-networks (experts) to improve efficiency and scale.
Model Evaluation
The process of measuring model performance, quality, and safety using defined metrics and test sets.
P
Parameters (Weights)
The internal variables a neural network learns during training. They act as the "knowledge" of the model.
Prompt Engineering
Designing and iterating prompts to guide AI models toward reliable, useful outputs.
Prompt Injection / Jailbreaking
A security vulnerability where malicious input causes an LLM to ignore its original instructions and execute unauthorized actions.
R
Red Teaming
The practice of aggressively testing AI systems by simulating adversarial attacks to identify flaws, biases, or vulnerabilities.
Retrieval-Augmented Generation (RAG)
A technique that augments model responses with retrieved context from trusted data sources.
T
Tokens & Tokenization
The base units of data processed by an LLM. A token is typically a chunk of characters rather than a full word.
Transformer Architecture
The neural network architecture introduced in 2017 that powers virtually all modern large language models through self-attention mechanisms.
Editorial standards
DoubleXL maintains a research-first approach: we use primary sources, align with global standards, and review content for accuracy before publishing.
- • Cite authoritative sources (standards bodies, peer-reviewed research, and official documentation).
- • Separate definitions from opinionated guidance and clearly label recommendations.
- • Re-validate entries when frameworks or best practices evolve.
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Citation policy
Sources are linked in every entry. Citations appear near section headings and in the sources list so readers can verify claims quickly without interrupting the reading flow.