AI Tools Glossary
Plain-English definitions for the terms you'll encounter when evaluating, buying, and using AI tools in 2026.
A
AI Overview
Google's AI-generated answer box that appears at the top of certain search results, summarizing information from multiple cited sources. Optimizing for inclusion in AI Overviews is a core part of GEO (Generative Engine Optimization).
Related: GEO, SERP
API
Application Programming Interface — a set of rules that lets different software applications communicate with each other. In AI tools, API access lets developers integrate a tool's capabilities directly into their own applications or workflows. Most enterprise-tier plans include API access; free and entry-level plans typically do not.
Related: Integration, Webhook
B
Brand Voice
A documented set of tone, style, and language guidelines that define how a brand communicates. In AI writing tools, Brand Voice features let users upload examples of existing content so the AI generates new content in a consistent style. Jasper AI's Brand Voice implementation is widely considered the most sophisticated in the category.
Related: Tone of Voice, Style Guide
C
Chunking
The process of breaking large documents into smaller, manageable segments for processing by AI models. Essential for handling text that exceeds a model's context window. Common strategies include fixed-size, semantic, recursive, and document-based chunking.
Related: Embedding, RAG, Token
Content Score
A 1–100 rating produced by SEO content tools (Surfer, Clearscope, Frase) that measures how closely a draft matches what's already ranking for a target keyword in terms of terms covered, structure, and depth.
Related: SERP
Context Window
The maximum amount of text (measured in tokens) an AI model can consider at once when generating a response. Larger context windows allow longer documents and richer conversations without truncation.
Related: Token, LLM
E
Embedding
A numerical representation of text that captures semantic meaning, used by AI models to compare similarity, perform retrieval, and power semantic search.
Related: Vector Database, RAG
F
Fine-tuning
Retraining a base AI model on domain-specific data to improve performance for a specialized task or brand voice. More expensive than prompting but can produce better, more consistent results at scale.
Related: LLM, Prompt Engineering
G
GEO
Generative Engine Optimization — the practice of optimizing content to be cited and surfaced by AI search engines like Google AI Overviews, Perplexity, and ChatGPT. The natural successor to traditional SEO for the AI search era.
Related: AI Overview, SEO
H
Hallucination
When an AI model generates information that sounds plausible but is factually incorrect or fabricated. Mitigations include RAG, retrieval grounding, and explicit fact-checking workflows.
Related: RAG, LLM
I
Integration
A connection between two software products that lets them share data or trigger actions. Native integrations are built into the tool; Zapier and Make integrations rely on third-party connectors.
Related: API, Webhook
Inference
The process of running a trained AI model to generate output from new input. Most pricing in AI tools ultimately reflects inference cost.
Related: LLM, Token
L
LLM
Large Language Model — the type of AI behind tools like ChatGPT, Claude, Gemini, and Jasper. LLMs are trained on large text corpora and generate output one token at a time.
Related: Token, Inference
N
NLP
Natural Language Processing — the field of AI focused on understanding and generating human language. Modern NLP is dominated by LLMs.
Related: LLM
P
Prompt
The input text given to an AI model to generate a response. Prompt quality has an outsized effect on output quality.
Related: Prompt Engineering
Prompt Engineering
The discipline of crafting prompts to reliably get high-quality output from AI models. Common techniques include role-setting, few-shot examples, structured output instructions, and chain-of-thought.
Related: Prompt, Zero-shot
R
RAG
Retrieval-Augmented Generation — a technique that pulls in external data at inference time to ground AI output in verified facts. Reduces hallucinations and lets models work with proprietary or up-to-date information.
Related: Embedding, Vector Database, Hallucination
S
SERP
Search Engine Results Page — the page Google (or another engine) returns for a query. SEO and GEO programs target visibility on the SERP.
Related: AI Overview, GEO
Semantic Search
Search based on meaning rather than exact keyword matches. Powered by embeddings and vector databases.
Related: Embedding, Vector Database
T
Token
The unit of text AI models process. Roughly 4 characters or 0.75 words in English. Both input and output tokens count against pricing and context limits.
Related: Context Window, Inference
Tone of Voice
The emotional and stylistic register of a brand's writing — formal, conversational, witty, authoritative. AI writing tools can be tuned to match a target tone via Brand Voice features.
Related: Brand Voice
V
Vector Database
A database that stores embeddings, used for semantic search and retrieval-augmented AI applications. Examples: Pinecone, Weaviate, pgvector.
Related: Embedding, RAG
W
Webhook
An automated message sent from one app to another when a specific event occurs. Used to trigger workflows in real time when something happens in an AI tool.
Related: API, Integration
Workflow
A sequence of automated AI tasks chained together. Copy.ai's Workflows feature is one of the most well-known examples — running research → outline → draft → social repurpose in a single pipeline.
Related: Integration
Z
Zero-shot
An AI model performing a task without any examples — purely from instructions in the prompt. Contrasts with few-shot, where examples are provided.
Related: Prompt Engineering