50 terms covering AI agents, LLMs, and developer infrastructure. Each definition is self-contained and quotable.
A/B testing compares two or more variants of a system by randomly assigning users to groups and measuring statistically significant differences in predefined outcome metrics.
MLOpsAn agent harness is the runtime environment that manages an AI agent's execution loop, tool access, permission boundaries, memory persistence, and conversation state.
Developer ToolsAn agent loop is the iterative cycle of observe, reason, act, and evaluate that an AI agent repeats until it completes a task or reaches a termination condition.
AI Agent DevelopmentAgent orchestration is the coordination layer that manages how multiple AI agents communicate, share context, delegate tasks, and resolve conflicts within a system.
AI Agent DevelopmentAgentic AI refers to artificial intelligence systems that autonomously plan, execute, and adapt multi-step tasks toward a goal without requiring human intervention at each step.
AI Agent DevelopmentAI agent memory is the system that persists information across interactions, enabling agents to recall past context, learn from experience, and maintain continuity between sessions.
AI Agent DevelopmentAI alignment is the research field dedicated to ensuring artificial intelligence systems reliably pursue goals that match human intentions, values, and ethical principles.
AI SafetyAn AI coding agent is an autonomous software development assistant that can read codebases, write code, run tests, debug errors, and commit changes with minimal human direction.
Developer ToolsAI guardrails are programmatic constraints and validation layers that prevent AI systems from generating harmful, off-topic, or policy-violating outputs during production use.
AI SafetyAn attention mechanism allows neural networks to dynamically focus on relevant parts of the input when producing each element of the output, weighting information by learned importance.
LLM ArchitectureA canary release gradually routes a small percentage of production traffic to a new version while monitoring for errors before expanding to all users.
DevOps/CI-CDContainer orchestration automates the deployment, scaling, networking, and lifecycle management of containerized applications across clusters of machines.
Cloud InfrastructureA content delivery network (CDN) distributes cached copies of web content across geographically dispersed servers to reduce latency and improve load times for users worldwide.
Cloud InfrastructureContext engineering is the practice of designing and optimizing the information provided to a language model to maximize the relevance, accuracy, and efficiency of its outputs.
LLM InfrastructureA context window is the maximum number of tokens a language model can process in a single input-output interaction, encompassing both the prompt and the generated response.
LLM InfrastructureContinuous deployment automatically releases every code change that passes automated testing directly to production without manual approval gates.
DevOps/CI-CDEdge computing processes data at or near the source of data generation rather than in a centralized data center, reducing latency and bandwidth consumption.
Cloud InfrastructureAn embedding is a dense numerical vector representation of text, images, or other data that captures semantic meaning in a format suitable for mathematical comparison and retrieval.
Machine LearningExperiment tracking systematically records machine learning training runs including hyperparameters, metrics, artifacts, and code versions to enable comparison and reproducibility.
MLOpsFeature flags are conditional switches in code that enable or disable functionality at runtime without deploying new code, decoupling deployment from feature release.
DevOps/CI-CDFine-tuning is the process of further training a pre-trained language model on a domain-specific dataset to improve its performance on targeted tasks without training from scratch.
Machine LearningFunction calling is an LLM capability that allows models to generate structured JSON arguments for predefined functions, enabling AI to interact with external systems and APIs.
AI Agent DevelopmentGenerative engine optimization is the practice of structuring web content to maximize its likelihood of being cited, quoted, or referenced by AI systems when generating answers.
Search & DiscoveryGitOps is an operational framework that uses Git repositories as the single source of truth for declarative infrastructure and application configuration with automated reconciliation.
DevOps/CI-CDInference is the process of running a trained machine learning model on new input data to generate predictions, classifications, or text outputs in real time.
LLM InfrastructureInfrastructure as Code (IaC) manages and provisions computing infrastructure through machine-readable configuration files rather than manual processes or interactive tools.
Cloud InfrastructureAn MCP server is a lightweight program that exposes tools, resources, and prompts to AI applications through the Model Context Protocol's standardized client-server interface.
Developer ToolsMixture of Experts (MoE) is a neural network architecture that routes each input to a subset of specialized sub-networks, enabling massive model capacity with efficient per-token computation.
LLM ArchitectureModel Context Protocol is an open standard that defines how AI applications connect to external data sources and tools through a unified client-server interface.
Developer ToolsModel distillation transfers knowledge from a large teacher model to a smaller student model by training the student to match the teacher's output distributions rather than hard labels.
LLM ArchitectureA model registry is a centralized repository that stores, versions, and manages machine learning model artifacts along with their metadata, lineage, and deployment status.
MLOpsModel serving deploys trained machine learning models as production services that accept inference requests and return predictions with low latency and high availability.
MLOpsA multi-agent system is an architecture where multiple specialized AI agents collaborate, communicate, and coordinate to solve problems that exceed any single agent's capabilities.
AI Agent DevelopmentMultimodal AI refers to systems that can process, understand, and generate content across multiple data types including text, images, audio, and video within a unified model.
Machine LearningRed teaming in AI involves systematically probing AI systems for vulnerabilities, biases, and failure modes by simulating adversarial attacks and edge-case scenarios.
AI SafetyRetrieval-augmented generation is an architecture that enhances language model outputs by retrieving relevant documents from external knowledge sources and including them in the model's context.
Search & DiscoveryRLHF (Reinforcement Learning from Human Feedback) trains AI models to align with human preferences by using human judgment as a reward signal to fine-tune model behavior.
AI SafetyServerless computing is a cloud execution model where the provider dynamically allocates resources and bills only for actual compute time used during function invocations.
Cloud InfrastructureStructured content is information organized with consistent formatting, semantic markup, and machine-readable metadata that enables automated processing by search engines and AI systems.
Search & DiscoveryA token budget is the allocated limit on input and output tokens for a language model request, used to control costs, latency, and context window utilization.
LLM InfrastructureTool use is a capability that allows language models to invoke external functions, APIs, or services by generating structured calls that are executed by the host application.
AI Agent DevelopmentThe transformer is a neural network architecture that uses self-attention mechanisms to process sequential data in parallel, forming the foundation of all modern large language models.
LLM Architecture