AI App Development
Prompt
A prompt is input sent to an AI model to describe a task, provide context, or request a response.
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AI App Development Glossary
Explore prompting, model selection, agents, MCP, RAG, structured outputs, evals, security, infrastructure costs and product UX for teams shipping AI applications.
ShouldEye explains AI application concepts for builders. It does not guarantee accuracy, security, savings or compliance for any specific system.
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485 published AI App Development definitions in this index
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AI and Decision Intelligence
Large Language Model
A Large Language Model is an AI system trained on vast text data to predict and generate human-like language responses.
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Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is an AI pattern that retrieves external evidence before generating an answer.
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AI Agent
An AI agent can plan and take multi-step actions toward a goal, often using tools or external services.
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Model Context Protocol
Model Context Protocol (MCP) is an open standard for connecting AI apps to tools and data sources in a structured way.
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Prompt Injection
A prompt injection attack steers an AI system by hiding instructions inside user content or retrieved text so the model ignores its intended rules.
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Structured Output
Structured output is model-generated data constrained or requested to follow a defined machine-readable shape.
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Time to First Token
Time to first token is the elapsed time from starting a model request until the first generated output token becomes available.
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Security and agency risks
AI and Decision IntelligenceAI security
Prompt Injection
A prompt injection attack steers an AI system by hiding instructions inside user content or retrieved text so the model ignores its intended rules.
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MCP Tool Poisoning
MCP tool poisoning is manipulation of a tool’s metadata or behavior to steer an AI host toward unsafe actions or disclosure.
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Denial-of-Wallet Attack
A denial-of-wallet attack deliberately drives excessive paid AI or tool usage to exhaust a victim’s budget or quota.
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Jailbreak
An AI jailbreak is an attempt to make a model bypass safety or policy constraints through crafted prompts or multi-step tricks.
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Guardrails
AI guardrails are policy filters and controls that constrain model inputs or outputs for safety and compliance.
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Excessive Agency
Excessive agency is the risk created when an AI system receives more autonomy, tools, permissions, or action scope than its task requires.
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AI and Decision IntelligenceFalse-confidence risk
AI Hallucination
An AI Hallucination is a fluent model output that presents false, fabricated, or unverifiable information as if it were true.
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RAG
RAG (retrieval-augmented generation) fetches documents at query time and conditions the model’s answer on that evidence.
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Prompt Engineering
Prompt engineering is the iterative design and testing of instructions and examples used to guide an AI model.
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Context Engineering
Context engineering is the design of what information an AI application selects, orders, labels, and supplies to a model.
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Excessive Agency
Excessive agency is the risk created when an AI system receives more autonomy, tools, permissions, or action scope than its task requires.
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Model-as-a-Judge
Model-as-a-judge uses an AI model to score, compare, or critique other model outputs against a rubric.
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Package Hallucination
Package hallucination is an AI coding assistant’s invention of a dependency name, version, import, or API that does not exist as described.
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Lost-in-the-Middle Problem
The lost-in-the-middle problem is reduced model use or recall of relevant information placed between the beginning and end of long context.
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AI and Decision IntelligenceAI integrity
Access Date
An access date records when a web source was retrieved, because online evidence can change or disappear after citation.
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Accountability
Accountability means named parties answer for AI system outcomes, evidence, and remedies.
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Adversarial Testing
Adversarial testing evaluates AI systems against crafted inputs designed to cause errors or unsafe outputs.
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Agent Decision Trace
An Agent Decision Trace is a reconstructible record of the inputs, policies, and steps an agent used to reach an action.
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Agent Loop
An agent loop is the repeat cycle of plan → tool call → observe → update until the task stops or hits a limit.
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Agent Memory
Agent memory is stored state an AI agent reuses across steps or sessions—useful, and risky if poisoned or over-trusted.
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Agent Trust
Agent trust is the confidence that an AI agent will act safely and correctly when verifying entities or completing tasks on a user’s behalf.
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Agent-Readable Trust
Agent-readable trust is trust information structured so an AI agent can parse, compare, and act on it without relying only on marketing copy.
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Agent-to-Agent Trust
Agent-to-Agent Trust is the confidence one autonomous agent places in another agent’s identity, permissions, and claims.
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Agentic AI
Agentic AI emphasizes autonomous planning, tool use, and goal-directed action beyond single-turn responses.
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Agentic Workflow
An agentic workflow lets an AI model choose or sequence some steps, tools, or branches toward a defined goal.
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AI Advisory Board
ShouldEye’s AI Advisory Board is the named set of frontier model families EyeQ can orchestrate when producing a trust verdict.
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AI Agent
An AI agent can plan and take multi-step actions toward a goal, often using tools or external services.
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AI Answer Drift
AI answer drift is when repeated asks about the same fact produce shifting claims, sources, or confidence without new evidence.
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AI Answer Manipulation
AI Answer Manipulation is deliberate distortion of what AI systems retrieve, cite, or conclude about an entity or claim.
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AI API
An AI API is a programmatic interface through which an application requests model inference or related AI services.
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AI Assurance
AI assurance is independent or structured evidence that AI systems meet stated risk, ethics, and performance claims.
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AI Audit
An AI audit is an examination of an AI system’s controls, evidence, and outcomes against defined criteria.
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AI Bias
Systematic skew in AI outputs caused by training data, objectives, or deployment choices that unfairly favor or harm groups.
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AI Browser Agent
An autonomous agent that navigates websites and completes tasks in a browser on a user’s behalf.
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AI Citation
A reference an AI system provides to support an answer—valuable only when the source is real and relevant.
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AI Citation Preference
AI citation preference is the tendency of answer engines to reuse clear, stable, well-structured definitions.
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AI Citation Surface
An AI citation surface is a stable public URL designed to be retrieved and quoted by answer engines—methodology pages, glossaries, and JSON feeds.
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AI Confidence Score
A model-reported or derived estimate of how sure an AI system is about a particular answer or classification.
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AI Dark Pattern
An AI dark pattern is a deceptive interface or adaptive behavior that steers users toward choices favoring the operator over informed intent.
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AI Deployer
An AI deployer is the entity that uses an AI system under its authority, except where the system is for personal non-professional activity.
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AI Developer
An AI developer is a person or organization that designs, trains, or builds AI models or systems.
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AI Distributor
An AI distributor makes an AI system available on the market without affecting its properties as provider.
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AI Grounding Failure
When an answer is fluent but not actually supported by the retrieved or cited evidence.
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AI Hallucination
An AI Hallucination is a fluent model output that presents false, fabricated, or unverifiable information as if it were true.
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AI Importer
An AI importer is an entity established in a jurisdiction that places on the market an AI system from a third-country provider.
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AI Inventory
An AI inventory is a maintained register of AI systems and models an organization uses or provides.
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AI Literacy
AI literacy is the understanding staff and users need to use and oversee AI systems responsibly.
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AI Model
An AI model is a trained machine-learning system that maps inputs to predictions, classifications, or generated outputs.
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AI Operator
An AI operator is an organization or team responsible for running and maintaining an AI system in production.
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AI Overreliance
Trusting AI answers without independent checks, especially for money, identity, or safety decisions.
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AI Provider
An AI provider is the entity that develops an AI system or model and places it on the market or puts it into service under its name.
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AI Regulatory Sandbox
An AI regulatory sandbox is a supervised environment for testing AI systems with regulator support under defined safeguards.
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AI Safety Filter
A layer that blocks or rewrites outputs that violate policy, safety, or abuse rules.
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AI SDK
An AI SDK is a software library that helps developers call models and manage common AI application patterns.
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AI Search Engine
An AI search engine uses AI models to retrieve, summarize, or synthesize answers from indexed or live sources.
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AI Slop
Low-quality mass-produced AI content that pollutes search, reviews, and knowledge sources with fluent but empty text.
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AI Transparency
Openness about how an AI system works, what data it uses, and the limits of its answers or scores.
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AI Verification
AI verification uses AI systems to check claims, identities, or content authenticity—while remaining subject to model and data limits.
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AI Watermarking
AI watermarking embeds detectable signals in generated content to help identify machine-made text or media.
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AI-Generated Content Label
An AI-generated content label is a visible or accessible notice that content was produced with AI.
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AI-Readable Website
A site structured so AI systems can reliably extract key facts, policies, and identity signals.
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Algorithmic Audit
An algorithmic audit specifically inspects algorithms and automated decision systems for bias, accuracy, and compliance issues.
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Educational information only. ShouldEye does not provide legal, banking, or individualized financial advice. Dispute rights, deadlines, and outcomes depend on your payment method, card network, issuer, processor, jurisdiction, and the facts of your case. Legal Disclaimers