Glossary
Agent
An autonomous entity that perceives its environment through sensors, processes information, makes decisions based on its programming and learned experiences, and acts upon the environment to achieve specific goals or objectives.
Agentic AI
AI systems designed to act autonomously as intelligent agents, capable of making decisions, taking actions, and pursuing goals with minimal human intervention. These systems can perceive their environment, reason about situations, and execute tasks independently while adapting to changing conditions.
Agentic RAG
Advanced Retrieval-Augmented Generation that embeds autonomous AI agents into the RAG pipeline. Unlike traditional RAG, agentic RAG allows AI agents to adapt retrieval processes dynamically, validate context, and use reflection, planning, tool use, and multiagent collaboration for more sophisticated information retrieval.
Agentic Services
Cloud-based or distributed services that leverage agentic AI capabilities to perform complex tasks autonomously. These services can handle multi-step workflows, integrate with various APIs and tools, and operate continuously to achieve specified objectives without constant human oversight.
API (Application Programming Interface)
A set of protocols and tools that allows different software applications to communicate with each other. AI services often provide APIs to enable integration with other systems and applications.
Artificial Intelligence (AI)
Computer systems designed to perform tasks that typically require human intelligence, including learning, reasoning, problem-solving, perception, and language understanding. AI encompasses various approaches from rule-based systems to machine learning and neural networks.
Attention Mechanism
A technique used in neural networks, especially transformers, that allows models to focus on relevant parts of input data when processing information. This mechanism enables models to weigh the importance of different inputs dynamically.
Autonomous Systems
Self-governing systems that can operate independently without human control, making decisions and taking actions based on their programming, sensors, and learned behaviors.
Backpropagation
A fundamental algorithm in training neural networks that calculates gradients by propagating errors backward through the network layers, enabling the model to adjust weights and improve performance.
Bias (AI)
Systematic errors or prejudices in AI systems that can lead to unfair outcomes. Bias can be introduced through training data, algorithm design, or deployment contexts, and addressing it is crucial for ethical AI development.
Chain-of-Thought
A prompting technique that encourages language models to break down complex reasoning into intermediate steps, improving performance on tasks requiring multi-step reasoning or problem-solving.
Computer Vision
A field of AI that enables computers to derive meaningful information from digital images, videos, and other visual inputs, and take actions or make recommendations based on that information.
Context Awareness
The capability of AI systems to understand and consider the situational context, history, and environment when making decisions or providing responses.
Context Window
The maximum amount of text (measured in tokens) that a language model can process at once. Larger context windows allow models to handle longer documents and maintain coherence over extended conversations.
Deep Learning
A subset of self-improving machine learning where AI algorithms are designed with a multi-layered, artificial neural network structure, allowing them to make more complex correlations compared to simpler machine learning-based systems.
Embedding
A mathematical representation of data (text, images, etc.) as vectors in a high-dimensional space, where semantically similar items are positioned closer together. Embeddings enable AI systems to understand and process meaning.
Few-Shot Learning
A machine learning approach where models learn to perform new tasks with only a few examples, leveraging prior knowledge to generalize from limited data.
Fine-tuning
The process of adapting a pre-trained AI model to perform specific tasks by training it on specialized datasets. This allows general-purpose models to become more effective for particular use cases.
Generative AI
AI models that learn patterns existing in training data and generate new examples that fit a particular pattern requested in the prompt. This includes text, images, audio, and video generation.
GPT (Generative Pre-trained Transformer)
A family of large language models developed by OpenAI that use transformer architecture and are pre-trained on vast amounts of text data to generate human-like text and perform various language tasks.
Gradient Descent
An optimization algorithm used to minimize loss functions in machine learning by iteratively adjusting model parameters in the direction that reduces error.
Hallucination
When AI models generate false or nonsensical information that appears plausible but is not grounded in their training data or provided context. A key challenge in ensuring reliable AI outputs.
Hyperparameter
Configuration variables set before training a machine learning model that control the learning process itself, such as learning rate, batch size, or number of layers.
Inference
The process of using a trained AI model to make predictions or generate outputs based on new input data. This is the operational phase where the AI applies its learned knowledge.
Intent Recognition
The ability of AI systems to understand and identify the underlying purpose or goal behind user inputs, enabling more accurate and helpful responses.
Large Language Model (LLM)
Advanced AI models trained on vast amounts of text data to understand and generate human-like language. Examples include GPT-4, Claude, and Gemini. These models form the foundation for many modern agentic AI systems.
Machine Learning (ML)
A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed for every scenario. ML algorithms build mathematical models based on training data to make predictions or decisions.
Model Architecture
The structural design of a neural network, defining how layers are arranged, connected, and how information flows through the system.
Multi-modal AI
AI systems capable of processing and understanding multiple types of data inputs simultaneously, such as text, images, audio, and video, to provide more comprehensive and contextual responses.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language in a valuable way. NLP powers chatbots, translation services, and text analysis tools.
Neural Network
A computing system inspired by biological neural networks, consisting of interconnected nodes (neurons) that process information. Deep neural networks with multiple layers form the basis of modern AI breakthroughs.
Overfitting
A modeling error where a machine learning model learns the training data too well, including noise and outliers, resulting in poor performance on new, unseen data.
Parameters
The internal variables of a machine learning model that are learned from training data. Large language models can have billions or trillions of parameters that encode knowledge.
Pre-training
The initial phase of training a large model on massive datasets to learn general patterns and knowledge before fine-tuning for specific tasks.
Prompt Engineering
The practice of designing and optimizing text prompts to elicit desired responses from AI language models. Effective prompting involves crafting clear instructions, providing context, and structuring requests for optimal AI performance.
RAG (Retrieval-Augmented Generation)
A technique that enhances language models by retrieving relevant information from external knowledge sources before generating responses, improving accuracy and enabling models to access up-to-date information.
Reinforcement Learning
A machine learning approach where agents learn to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. This method is crucial for training autonomous agents.
Scalability
The ability of AI systems and agentic services to handle increasing workloads, users, or complexity without significant degradation in performance or efficiency.
Semantic Search
A search technique that understands the intent and contextual meaning of search queries rather than just matching keywords, enabling more relevant and intelligent search results.
Supervised Learning
A machine learning approach where models are trained on labeled data, learning to map inputs to known outputs. This is used for tasks like classification and regression.
Temperature (AI)
A parameter that controls the randomness of AI model outputs. Lower temperatures produce more deterministic responses, while higher temperatures increase creativity and variation.
Token
The basic unit of text that language models process. A token can be a word, part of a word, or punctuation. Models have token limits that define their context window size.
Training Data
The information used to teach AI models how to perform specific tasks. Quality and diversity of training data significantly impact the performance and capabilities of AI systems.
Transfer Learning
A technique where knowledge gained from training on one task is applied to a different but related task, enabling efficient learning with less data.
Transformer Architecture
A neural network architecture that revolutionized NLP and forms the foundation of modern language models. Transformers use attention mechanisms to process and understand relationships in sequential data.
Unsupervised Learning
Machine learning approaches where models find patterns in data without labeled examples, including clustering and dimensionality reduction techniques.
Vector Database
Specialized databases designed to store and efficiently search high-dimensional vector embeddings. Essential for RAG systems, semantic search, and similarity matching in AI applications.
Zero-Shot Learning
The ability of AI models to perform tasks without specific training examples, relying on general knowledge and instructions provided in prompts.