Each one explained plainly first, then precisely. You should never need this page to get an answer from us.
An AI trained on huge amounts of text that can read, write, and reason in language.
Technical: A transformer-based neural network with billions of parameters trained on broad text corpora, producing next-token predictions and capable of in-context learning.
Example: Drafting a client email or summarizing a 30-page contract in seconds.
AI that creates new content — text, images, audio, video, code.
Technical: Models that learn the distribution of training data and sample from it to produce novel outputs (LLMs, diffusion models, multimodal models).
Example: Generating ad creative variants or proposal first drafts.
An AI that can plan, use tools, and complete multi-step tasks — not just answer.
Technical: An orchestrated system combining an LLM, tool/function calling, memory, planning, and human-in-the-loop checkpoints.
Example: An agent that pulls a lead from CRM, drafts an email, waits for approval, and sends.
AI that answers using your own documents instead of just what it was trained on.
Technical: Indexing documents into embeddings, retrieving top-k chunks at query time, and passing them as grounded context to the model.
Example: An internal assistant that answers HR questions from your policy PDFs.
A database that stores meaning, not just words — so AI can find similar content.
Technical: A datastore for high-dimensional vectors with approximate nearest-neighbor search (HNSW, IVF).
Example: Pinecone or Weaviate powering a knowledge-base assistant.
Numerical fingerprints of text or images that capture meaning.
Technical: Dense vector representations produced by an embedding model, enabling semantic similarity search and clustering.
Example: Used to match a user's question to the most relevant SOP paragraph.
Writing instructions that get AI to produce the right output reliably.
Technical: Designing system prompts, few-shot examples, output schemas, and tool definitions to steer model behavior.
Example: A reusable prompt that turns sales call transcripts into 3-bullet briefs.
A way for software to talk to other software.
Technical: A documented interface (HTTP, gRPC, SDK) exposing functions of a service for programmatic access.
Example: Using the OpenAI API to summarize support tickets in your CRM.
Letting software do work that humans used to do by hand.
Technical: Event-driven workflows that trigger actions across systems, often combining APIs, conditional logic, and AI steps.
Example: Auto-tagging incoming leads and routing them to the right rep.
A sequence of steps to complete a task.
Technical: A directed graph of steps with triggers, conditions, parallel branches, retries, and human approvals.
Example: Quote → review → e-sign → invoice → CRM update.
When AI confidently makes something up.
Technical: Plausible but unfaithful outputs caused by missing grounding, decoding bias, or out-of-distribution prompts.
Example: An AI citing a court case that doesn't exist.
The AI itself — the program that does the thinking.
Technical: A trained set of parameters that maps inputs to outputs (LLM, diffusion model, classifier, etc.).
Example: Choosing GPT-5 vs. Claude vs. Gemini for a workload.
A small chunk of text — roughly a word or part of a word.
Technical: The atomic unit a model processes, produced by a tokenizer (BPE, SentencePiece).
Example: Pricing and context limits are measured in tokens.
Training a general AI further on your own data to make it specialized.
Technical: Supervised or preference-based updates to a base model's weights using domain-specific data.
Example: Fine-tuning on past proposals so AI writes in your firm's voice.
AI that handles more than one kind of input — text, images, audio, video.
Technical: Models with shared representations across modalities, enabling cross-modal reasoning.
Example: Asking an AI about a chart in a PDF.
The rules and oversight that make AI safe to use at work.
Technical: Frameworks for model risk, data classification, access control, audit trails, and regulatory alignment.
Example: A policy saying client PHI cannot be sent to public AI tools.
A human reviews or approves AI output before it acts.
Technical: An approval checkpoint inserted into an automated workflow, often with structured feedback capture.
Example: AI drafts a refund response; a manager approves before send.
When an AI uses an external tool — like a search, a database, or a calendar.
Technical: Function/tool invocation where the model emits a structured call the runtime executes and returns results into context.
Example: An agent calling your CRM API to update a deal stage.
Protecting the personal and confidential information you handle.
Technical: Data minimization, access control, encryption at rest/in transit, retention policies, and regulatory frameworks (GDPR, HIPAA, CCPA).
Example: Choosing tools with zero-retention policies for sensitive prompts.
How prepared your company is to actually benefit from AI.
Technical: A multi-dimensional assessment of data, workflows, tools, team, governance, and execution capacity.
Example: Your AI Masterwork Score™ across 10 dimensions.
