✷Reference
AI Automation Glossary
Clear definitions for AI, automation, and intelligent systems terminology used by working operators.
A
AI Automation
The use of artificial intelligence to perform tasks that previously required human intervention. In business contexts, this typically involves automating repetitive workflows, document processing, and decision-making processes.
Learn more about AI consulting servicesAgent / AI Agent
An AI system that can plan steps, use tools, and take actions toward a defined goal. In business workflows, agents often coordinate data lookup, document handling, system updates, and human escalation.
Learn more about AI agent catalogueA/B Testing (LLM)
A controlled experiment comparing two model, prompt, retrieval, or workflow variants. In LLM systems, A/B tests should measure quality, safety, latency, cost, and downstream business impact.
Learn more about automation decision criteriaAI Governance
The policies, roles, controls, and review processes that determine how AI is selected, deployed, monitored, and retired. Governance helps organizations manage risk without blocking useful automation.
Learn more about CloudNSiteAudit Trail
A chronological record of user actions, system decisions, data access, and workflow changes. Audit trails are essential for regulated AI systems that need traceability and post-event review.
Learn more about auditable AI systemsB
Business Associate Agreement (BAA)
A contract required under HIPAA between a covered entity and a business associate. Essential for AI systems that process healthcare data, ensuring that AI automation tools appropriately safeguard protected health information.
Learn more about Private LLM for healthcareBias Audit
A review process that evaluates whether an AI system produces unfair or systematically different outcomes across groups. Bias audits require representative data, clear metrics, and a plan for remediation when issues are found.
C
Context Window
The amount of text, tokens, or multimodal input a model can consider in a single request. Larger context windows can help with long documents, but they also increase cost and make retrieval strategy more important.
Chunking
The process of splitting long documents into smaller sections for retrieval or model input. Good chunking preserves enough context for accuracy without flooding the model with irrelevant text.
Learn more about private LLM tradeoffsChain-of-Thought (CoT)
A prompting and evaluation concept where intermediate reasoning steps are represented to improve complex task performance. In production systems, teams often prefer concise rationales or tool traces rather than exposing raw reasoning to users.
Cost per Token / Cost per Inference
A unit economics measure for how much it costs to run model requests. Tracking this cost helps teams choose between public APIs, private models, caching, smaller models, and workflow redesign.
Learn more about AI deployment cost tradeoffsCCPA
The California Consumer Privacy Act, a privacy law covering consumer rights around personal information. AI workflows that process California resident data must account for access, deletion, disclosure, and opt-out obligations.
Conversational AI
AI systems designed to interact through chat, messaging, or voice. In business settings, conversational AI supports customer service, intake, scheduling, internal help desks, and guided workflows.
Learn more about customer service automationD
Distillation
A training approach where a smaller model learns from a larger model's outputs or behavior. Teams use distillation to reduce latency and cost while preserving enough quality for a defined workflow.
Drift Detection
Monitoring for changes in input data, user behavior, model outputs, or business rules that reduce AI system quality. Drift detection helps teams know when retraining, prompt updates, or process changes are needed.
De-identification
The process of removing or transforming identifiers so data is less likely to reveal a specific person. De-identification can reduce privacy risk, but it must be validated against the data type and use case.
Learn more about healthcare AI workflowsData Residency
A requirement or policy that data stays within a specific country, region, cloud, or controlled environment. Data residency affects AI vendor selection, model hosting, backups, and support workflows.
Learn more about private AI deployment choicesDocument AI
AI systems that classify, extract, summarize, compare, or route documents. It extends basic OCR by using models and business rules to understand document structure and intent.
Learn more about AI document automationE
Embedding
A numerical representation of text, images, or other data that captures semantic meaning. Embeddings power search, clustering, recommendations, and retrieval-augmented generation systems.
Learn more about private AI search systemsEdge AI
AI that runs close to where data is generated, such as on a device, facility server, or local gateway. Edge deployment can reduce latency, bandwidth use, and exposure of sensitive data.
Learn more about private AI architectureEval / Evaluation Harness
A repeatable testing framework for measuring an AI system against expected behavior. Evaluation harnesses often include task datasets, grading rules, regression checks, and human review loops.
Learn more about AI implementationF
Foundation Model
A large general-purpose model trained on broad data and adapted for many downstream tasks. LLMs, vision-language models, and speech models are common foundation models used as the base layer for business AI systems.
Fine-tuning
The process of further training a model on domain-specific examples so it better matches a task, format, or vocabulary. It is most useful when prompt design and retrieval alone cannot produce consistent behavior.
Learn more about private AI deploymentFew-shot Prompting
A prompting technique that includes a small set of examples to show the model the desired pattern. It is useful for classification, extraction, formatting, and tone control when fine-tuning is unnecessary.
G
Grounding
The practice of tying model outputs to trusted sources, records, or retrieved context. Grounding is essential when AI systems answer questions about contracts, policies, healthcare data, or customer records.
Learn more about private AI systemsGuardrails
Policies, validations, filters, and workflow controls that constrain AI behavior. Guardrails reduce risk by blocking unsafe actions, enforcing formats, and routing uncertain outputs to humans.
Learn more about secure AI deploymentGolden Dataset
A curated set of representative examples with trusted answers or labels. Golden datasets are used to test model changes, retrieval updates, prompt revisions, and automation quality before release.
GDPR
The European Union's General Data Protection Regulation, which governs how personal data is collected, processed, retained, and transferred. AI systems touching EU personal data need privacy-by-design controls and clear processing purposes.
H
HIPAA
Health Insurance Portability and Accountability Act. Critical framework for healthcare AI implementations, governing how AI systems can process, store, and analyze medical information while maintaining patient privacy.
Learn more about HIPAA-ready architecture AI solutionsHybrid Search
A retrieval approach that combines keyword search with vector or semantic search. Hybrid search is often more reliable than either method alone for enterprise knowledge bases.
Learn more about document workflow automationHallucination
An AI output that is unsupported, fabricated, or inconsistent with the available evidence. RAG, grounding, evals, and human review help reduce hallucination risk in production workflows.
Learn more about LLM deployment choicesHITRUST
A certifiable security and risk management framework often used by healthcare and regulated organizations. HITRUST can influence vendor selection and control requirements for healthcare AI systems.
Learn more about healthcare AI controlsI
Inference
The process of running a trained model to produce an output from new input. In production AI systems, inference cost, latency, and reliability are major operational concerns.
Inference Server
Infrastructure that hosts a model and serves predictions through an API or internal endpoint. For private AI, inference servers must be sized, secured, monitored, and upgraded like other production services.
Learn more about private model hostingIntelligent Document Processing (IDP)
AI-assisted extraction, classification, validation, and routing of information from documents. IDP is used for invoices, claims, contracts, intake packets, and records-heavy operations.
Learn more about document processing automationK
Knowledge Base
A structured or semi-structured collection of documents, answers, policies, and operational knowledge. Knowledge bases become more useful when paired with semantic search, RAG, and clear ownership for updates.
Learn more about knowledge workflow automationKnowledge Graph
A representation of entities and relationships, such as customers, products, claims, providers, or policies. Knowledge graphs help AI systems reason over connected facts rather than isolated documents.
L
Large Language Model (LLM)
A type of artificial intelligence that uses deep learning and large datasets to understand, summarize, generate, and predict content. Examples include GPT, Claude, and Llama.
Learn more about Private LLM deploymentLoRA (Low-Rank Adaptation)
A parameter-efficient fine-tuning technique that trains small adapter weights instead of updating an entire model. LoRA can reduce infrastructure cost when teams need specialized private models.
Learn more about private LLM solutionsLLMOps
Operational practices specific to LLM applications, including prompt management, evals, retrieval quality, model routing, tracing, and cost control. LLMOps turns prototypes into systems that can be audited and improved.
Learn more about LLM operationsLatency
The time between submitting a request and receiving a response. Low latency is important for voice agents, live chat, and operational workflows where users are waiting for the AI system.
LLM-as-Judge
An evaluation method where a model grades another model's output against a rubric. It can accelerate review at scale, but high-stakes workflows still need calibration, spot checks, and human oversight.
Learn more about AI evaluation strategyM
Machine Learning (ML)
A subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. ML algorithms build models based on training data to make predictions.
Learn more about AI consultingMulti-Agent System
A system where multiple AI agents divide responsibilities, share context, or review one another's work. This pattern is useful when a workflow needs separate research, extraction, validation, and routing steps.
Learn more about AI agent workflowsModel Context Protocol (MCP)
An open protocol for connecting AI applications to external tools, files, and data sources through standardized servers. MCP helps teams expose business systems to agents without building one-off connectors for every model client.
MLOps
Operational practices for deploying, monitoring, and maintaining machine learning systems. MLOps covers model versioning, data pipelines, testing, release management, and incident response.
Learn more about AI implementation planningModel Gateway / API Gateway
A routing layer that controls access to one or more AI models or providers. Gateways help teams enforce policy, track usage, manage fallbacks, and switch between private and public models.
Learn more about private LLM versus public APIModel Versioning
The practice of tracking model, prompt, dataset, and configuration versions used in production. Versioning makes changes auditable and allows teams to roll back when quality or compliance issues appear.
Learn more about AI governance planningO
Observability (LLM)
The ability to inspect how an LLM application behaves in production, including prompts, retrieved context, tool calls, costs, latency, and errors. Observability is required for debugging and compliance review.
Learn more about LLM operationsOCR (Optical Character Recognition)
Technology that converts printed, handwritten, or scanned text into machine-readable text. OCR is often the first step before classification, extraction, summarization, or downstream workflow automation.
Learn more about document workflow automationOrchestration
The coordination layer that decides which steps, tools, models, and human reviews happen in a workflow. Orchestration is what turns individual AI capabilities into an end-to-end business process.
Learn more about agent orchestrationP
PHI
Protected Health Information. Any information about health status, provision of healthcare, or payment for healthcare that can be linked to a specific individual. AI systems handling PHI must meet strict security and privacy requirements.
Learn more about Healthcare AI solutionsPrivate LLM
A large language model deployed within a controlled environment where data never leaves the organization's infrastructure. Used by healthcare, financial, and government organizations to use AI while maintaining data privacy.
Learn more about Private LLM solutionsPrompt Engineering
The practice of designing instructions, examples, and constraints that guide model behavior. In production systems, prompt engineering is paired with testing, retrieval, guardrails, and monitoring.
Learn more about AI consultingPII (Personally Identifiable Information)
Information that can identify, contact, or distinguish a person, either directly or when combined with other data. AI systems processing PII need access controls, retention rules, and privacy review.
Learn more about private AI safeguardsQ
Quantization
A model optimization method that stores weights with lower numerical precision. Quantization can make self-hosted models faster and cheaper to run, especially on constrained GPU or edge environments.
Learn more about self-hosted AI architectureR
RAG
Retrieval-Augmented Generation. A technique that combines retrieval of relevant documents with text generation, allowing AI systems to provide accurate responses based on specific data sources.
Learn more about AI implementationRPA
Robotic Process Automation. Software technology that makes it easy to build, deploy, and manage software robots that emulate human actions interacting with digital systems and software.
Learn more about Workflow automationReranking
A second-stage retrieval step that reorders candidate results by relevance before they are passed to a model. Reranking improves RAG quality by reducing irrelevant context.
Learn more about AI workflow automationReAct (Reasoning + Acting)
An agent pattern that alternates between reasoning about a task and taking actions through tools. ReAct-style systems are useful when the model must gather information, call APIs, and revise its next step.
Learn more about AI agent implementationResponsible AI
A discipline focused on building and operating AI systems with attention to safety, fairness, privacy, accountability, and transparency. It becomes practical through governance, testing, documentation, and escalation paths.
Red Teaming (AI)
Structured adversarial testing that probes an AI system for unsafe behavior, prompt injection, policy bypasses, data leakage, or workflow abuse. Red teaming is especially important before exposing agents to customers or sensitive systems.
Learn more about AI risk reviewS
SOC 2
Service Organization Control 2. An auditing framework ensuring AI service providers securely manage data, increasingly important for organizations deploying AI automation and LLM systems.
Learn more about Enterprise AI solutionsSOC 2 Type I
A SOC 2 report that evaluates the design of security controls at a specific point in time. Important for AI vendors to demonstrate proper security controls are in place for AI systems handling sensitive data.
Learn more about Enterprise AISOC 2 Type II
A SOC 2 report that evaluates both the design and operating effectiveness of security controls over a period of time, typically 6-12 months. The gold standard for AI service providers handling enterprise data.
Learn more about Enterprise AI servicesSemantic Search
Search that matches meaning rather than only exact keywords. It helps users find relevant policies, tickets, products, or records even when they phrase a query differently from the source document.
Learn more about workflow automationSystem Prompt
A high-priority instruction that defines an AI application's role, boundaries, tools, and expected behavior. System prompts are a core control surface for enterprise assistants and agents.
Self-Hosted LLM
A large language model deployed on infrastructure controlled by the organization or its trusted environment. Self-hosting is common when data privacy, compliance, predictable cost, or customization matter.
Learn more about self-hosted LLM deploymentShadow Deployment
A release method where a new model or workflow runs alongside production without affecting users. Teams compare outputs and operational behavior before deciding whether to promote the new system.
T
Tool Use / Function Calling
A model capability that lets an AI system call approved tools, APIs, or functions instead of only producing text. It is central to production agents that need to read records, update systems, or trigger workflows.
Learn more about custom AI agentsTransformer
A neural network architecture that uses attention mechanisms to process sequences of text, code, images, or other data. Modern LLMs rely on transformer architectures because they handle long-range patterns efficiently.
Token
A unit of text processed by a language model, often a word fragment rather than a full word. Token counts affect context limits, latency, and cost for LLM applications.
Throughput
The amount of work a system can process in a given time, such as requests per second or tokens per minute. Throughput matters when AI workflows run at customer-support, document-intake, or batch-processing scale.
Tracing (LLM)
A record of the steps an LLM application took to produce an output, including prompts, retrieval calls, tool calls, and intermediate responses. Tracing helps diagnose failures in multi-step agent workflows.
Learn more about AI workflow designV
Vector Database
A database optimized for storing embeddings and finding semantically similar items. Vector databases are common infrastructure for RAG, knowledge search, and customer support automation.
Learn more about private AI infrastructureVoice AI / Voice Agent
A conversational AI system that uses speech recognition, language understanding, and speech synthesis to handle phone or voice interactions. Voice agents require careful latency, escalation, and compliance design.
Learn more about AI agentsW
Workflow Automation
The use of software and AI to move work through a sequence of steps with less manual effort. Modern workflow automation often combines rules, integrations, document processing, and human-in-the-loop review.
Learn more about workflow automation servicesZ
Zero Data Retention (ZDR)
A provider configuration or contract term stating that submitted data is not stored after processing. ZDR can reduce exposure, but teams still need to validate logs, subprocessors, and connected workflow tools.
Learn more about public API privacy tradeoffs