00 — Translate
Same concepts, different names
The industry is converging on these building blocks.
| Concept | Claude | ChatGPT | Microsoft Copilot | Gemini | Perplexity | Grok |
|---|---|---|---|---|---|---|
| Strengths | Reasoning and judgement | Broad use and execution | Microsoft Suite native, Outlook | Image generation, Google Suite native | Real-time events & research | X (Twitter) |
| Conversational chat | Chat | Chat | Copilot Chat | Gemini app | Ask / Search | Grok chat |
| Persistent workspace with files | Projects | Projects | Notebooks / Pages | NotebookLM | Spaces | Workspaces |
| Editable content surface | Artifacts | Canvas | Pages | Canvas | Pages | Canvas |
| Reusable custom assistant | Skills | Custom GPTs | Agents (Copilot Studio) | Gems | Spaces (custom instructions) | Skills |
| Deep research | Research | Deep Research | Researcher | Deep Research | Deep Research | DeepSearch |
| Reasoning / “think” mode | Extended thinking | Thinking (reasoning) | Think Deeper | Deep Think | Reasoning (Pro) | Think mode |
| Voice mode | Voice | Advanced Voice | Copilot Voice | Gemini Live | Voice | Voice mode |
| Image / media generation | Charts/SVG via Artifacts | Images, Sora (video) | Designer | Imagen, Veo | — | Imagine |
| Memory / personalization | Memory | Memory (Dreaming) | Copilot Memory | Personal Intelligence | Memory | Memory |
| Desktop / computer-use agent | Cowork, Claude for Chrome | ChatGPT Agent, Codex app | Copilot Cowork, Actions | Gemini Agent | Comet, Personal Computer | Grok Agents (beta) |
| Code & data analysis | Claude Code | Codex (GPT-5.3) | Python in Excel | Colab / Code Assist | Labs | Grok Build (Code) |
| External system connectors | MCP | Connectors / Apps | Graph connectors | Extensions | Connectors | API tools, X search |
| Agent builder / no-code agents | Agent SDK | AgentKit | Copilot Studio | Vertex AI Agent Builder | Agent API | Agents API |
| Embedded in daily apps | Word/Excel/PPT add-ins, Chrome | Desktop app, browser | Native in Word/Excel/PPT/Outlook, Edge | Workspace: Docs/Sheets/Slides/Gmail, Chrome | Comet browser + extension | In X (Twitter), apps |
Which one, when?
| Tool | Best use | Don’t use it for |
|---|---|---|
| ChatGPT | Execution, file work, drafting, coding, multimodal workflows, getting things done fast | Subtle taste work where you want a long strategic sparring partner |
| Claude | Deep synthesis, judgment, long-context thinking, writing taste, strategic pressure-testing | Fast tool-heavy execution when speed and integrations matter more |
| Microsoft Copilot | Work inside Word, Excel, PowerPoint, Outlook, Teams, and Microsoft files | General creative thinking outside the Microsoft ecosystem |
| Gemini | Gmail, Docs, Sheets, Meet, NotebookLM, Google Drive context, research across Google Workspace | Polished strategic writing if you prefer Claude’s taste |
| Perplexity | Fast cited research, current topic scanning, source discovery, “what’s the state of this?” | Final judgment, sensitive internal work, or polished writing |
| Grok | Real-time culture, X/Twitter-native signal, edgy brainstorming, fast web-aware takes | Careful enterprise work, high-stakes factual synthesis, or polished client-ready output |
01 — Basics
Core Concepts
| Term | Plain-English meaning | Why it matters |
|---|---|---|
| Artificial Intelligence (AI) | Software that performs tasks that usually require human judgment, pattern recognition, language, or decision-making. | The umbrella term for tools that can analyze, generate, classify, recommend, or automate work. |
| Machine Learning (ML) | A type of AI where systems learn patterns from data instead of being explicitly programmed for every rule. | Powers forecasting, recommendations, fraud detection, pricing models, and many modern AI systems. |
| Deep Learning | Machine learning that uses layered neural networks to detect complex patterns. | Drives image recognition, speech recognition, translation, and many large language models. |
| Neural Network | A model loosely inspired by the brain, made of connected layers that transform inputs into predictions or outputs. | The foundation of many advanced AI systems. |
| Generative AI | AI that creates new content, such as text, images, code, audio, video, or synthetic data. | Useful for drafting, brainstorming, design, customer support, research, and workflow acceleration. |
| Predictive AI | AI that estimates what is likely to happen based on historical data. | Common in forecasting, credit risk, demand planning, churn prediction, and investment research. |
02 — LLMs
Large Language Models
| Term | Plain-English meaning | Why it matters |
|---|---|---|
| Large Language Model (LLM) | An AI model trained on large amounts of text to understand and generate language. | The engine behind tools like ChatGPT, Claude, Gemini, and many AI assistants. |
| Foundation Model | A large general-purpose AI model that can be adapted to many tasks. | Companies often build specialized apps on top of foundation models rather than training from scratch. |
| Multimodal Model | An AI model that can work with more than one type of input or output, such as text, images, audio, video, or files. | Enables use cases like analyzing PDFs, interpreting charts, reading screenshots, and generating media. |
| Context Window | The amount of information an AI model can consider at once during a conversation or task. | A larger context window lets the model work with longer documents, more history, or bigger datasets. |
| Token | A small unit of text processed by an AI model, often part of a word or phrase. | AI usage and cost are often measured in tokens. |
| Prompt | The instruction, question, or context given to an AI model. | Better prompts usually produce better outputs, especially when they include role, goal, context, constraints, and examples. |
| System Prompt | Higher-priority instructions that guide how an AI assistant should behave. | Helps enforce tone, safety rules, workflow steps, and brand or company standards. |
03 — Under the hood
How AI Produces Answers
| Term | Plain-English meaning | Why it matters |
|---|---|---|
| Training | The process of teaching a model patterns from large datasets. | Determines the model’s general capabilities and limitations. |
| Inference | The process of using a trained model to produce an answer, prediction, or output. | This is what happens when a user asks an AI tool a question. |
| Fine-Tuning | Further training a model on a narrower dataset to improve performance on specific tasks. | Can make AI better at a company’s tone, domain, or workflow, but requires good data and governance. |
| Embedding | A numerical representation of text, images, or other data that captures meaning and similarity. | Used for search, recommendations, clustering, and retrieval-based AI tools. |
| Retrieval-Augmented Generation (RAG) | A method where an AI system retrieves relevant documents or data before generating an answer. | Helps AI answer using company files, policies, research, or databases without retraining the model. |
| Vector Database | A database designed to store and search embeddings. | Often used in AI search, knowledge-base assistants, and RAG systems. |
| Agent | An AI system that can plan steps, use tools, and take actions toward a goal. | Useful for workflows like research, reporting, customer support, coding, scheduling, and data analysis. |
| Tool Use | When an AI model calls external tools, such as search, calculators, databases, email, calendars, or code execution. | Makes AI more useful and more accurate because it can act on real systems or fetch current information. |
04 — Risk & governance
Quality, Risk, and Governance
| Term | Plain-English meaning | Why it matters |
|---|---|---|
| Hallucination | When an AI produces information that sounds plausible but is wrong or unsupported. | A major risk in research, legal, finance, medical, and customer-facing use cases. |
| Grounding | Tying an AI answer to reliable sources, documents, data, or citations. | Reduces hallucinations and makes outputs easier to verify. |
| Evaluation (Eval) | A structured test of how well an AI system performs on specific tasks. | Helps teams compare models, monitor quality, and catch regressions. |
| Benchmark | A standardized test used to compare AI model performance. | Useful for directional comparison, but real business tasks often need custom evaluations. |
| Bias | Systematic unfairness or skew in AI outputs due to data, design, or deployment choices. | Can create legal, reputational, and ethical risk. |
| Explainability | The ability to understand why an AI system produced a result. | Important for regulated or high-stakes decisions. |
| Human in the Loop | A workflow where humans review, approve, or correct AI outputs. | Helps manage risk while still gaining productivity. |
| Guardrails | Rules, filters, or controls that limit what an AI system can do or say. | Protects against unsafe outputs, data leaks, policy violations, and brand risk. |
| Data Privacy | Protecting sensitive, personal, or confidential information used with AI systems. | Essential when AI touches customer data, company strategy, financials, or employee information. |
05 — Business
Business and Implementation Terms
| Term | Plain-English meaning | Why it matters |
|---|---|---|
| Use Case | A specific business problem or workflow where AI can create value. | Good AI adoption starts with clear use cases, not vague experimentation. |
| Workflow Automation | Using software, sometimes with AI, to complete repeatable steps with less manual effort. | Can reduce time spent on reporting, routing, drafting, analysis, and admin tasks. |
| Copilot | An AI assistant that helps a human perform work but does not fully own the process. | Common in writing, coding, finance, CRM, email, and productivity tools. |
| Autonomous System | An AI-enabled system that can operate with limited human intervention. | Higher leverage, but also higher risk and governance needs. |
| Model Provider | A company that builds or hosts AI models. | Examples include OpenAI, Anthropic, Google, xAI, and others. |
| API | A software connection that lets one system use another system’s capabilities. | Many businesses access AI models through APIs rather than standalone chat apps. |
| Latency | The time it takes for an AI system to respond. | Important for user experience, customer support, trading workflows, and real-time applications. |
| Model Cost | The cost to run AI tasks, often based on usage volume, input size, output size, and model choice. | Affects whether an AI use case can scale economically. |
| Model Drift | When an AI system’s performance changes or degrades over time as data, users, or conditions change. | Requires monitoring, evaluation, and periodic updates. |
06 — Interfaces & formats
Ways People and Systems Use AI
| Term | Plain-English meaning | Simple example |
|---|---|---|
| Chat App | A conversational interface where a person types or speaks to an AI assistant. | ChatGPT, Claude, Gemini, Grok, or Perplexity in a browser or mobile app. |
| API (Application Programming Interface) | A software connection that lets one product use another product’s capabilities. | A company connects its customer-support tool to OpenAI or Anthropic so tickets can be summarized automatically. |
| CLI (Command-Line Interface) | A text-based tool used from a terminal instead of a graphical app. | A developer types a command to ask an AI coding agent to inspect a software project. |
| IDE | A coding workspace where developers write, run, and debug software. | VS Code, Cursor, JetBrains, or Xcode with an AI coding assistant built in. |
| SDK (Software Development Kit) | A packaged set of code helpers that makes it easier for developers to use an API. | Instead of manually calling an AI API, a developer installs the provider’s Python or JavaScript SDK. |
| MCP (Model Context Protocol) | An open standard for connecting AI apps to external tools, files, databases, and workflows. | Claude or ChatGPT connects to Google Drive, local files, or a company database through an MCP server. |
| MCP Client | The AI app or environment that connects to MCP servers. | Claude Desktop, ChatGPT, or a coding tool that can discover and use MCP tools. |
| MCP Server | The connector that exposes a tool, data source, prompt, or workflow to an AI app. | A Gmail MCP server might let an AI search email, draft replies, or label messages if authorized. |
| Connector | A prebuilt integration between an AI tool and another app or data source. | A Google Drive connector lets an AI read selected Docs, Sheets, Slides, or PDFs. |
| Plugin | An add-on that gives an AI tool new capabilities, workflows, or integrations. | A plugin might add a research workflow, a document generator, or a specialized finance analysis routine. |
| Skill | A reusable instruction package that teaches an AI how to perform a particular kind of work. | A “meeting notes formatter” skill could turn raw notes into decisions, action items, and follow-up emails. |
| Markdown / MD File | A plain-text document format that uses simple symbols for headings, lists, links, and tables. | This term sheet is an .md file, so it is easy to edit, share, convert to PDF, or publish on a website. |
| JSON | A structured data format often used by APIs and AI tools. | An AI can return customer records as JSON so another system can read them cleanly. |
| CSV | A simple spreadsheet-style file format where values are separated by commas. | A company exports sales data as a CSV and asks an AI to summarize trends. |
07 — Providers
Major AI Product Families
Product and model names change quickly. Use this as a map of the major families, with links to each provider’s current model or product pages instead of static model lists.
| Provider / family | Main products | Latest links | Best mental model |
|---|---|---|---|
| Anthropic Claude | Claude chat, Claude Code, Claude Cowork, Claude Platform/API, Claude for Chrome, Claude for Microsoft 365. | Claude models, Claude products, Claude Cowork. | A family of assistants and agents known for writing, analysis, reasoning, coding, long documents, and cautious enterprise workflows. |
| OpenAI ChatGPT / Codex | ChatGPT, OpenAI API, Codex, ChatGPT apps and connectors, image/audio/video tools depending on plan. | OpenAI models, Codex models, Codex product. | ChatGPT is the general assistant; Codex is the software-building agent; the API lets companies build AI into their own products. |
| Google Gemini | Gemini app, Gemini API, Google AI Studio, Vertex AI / Google Cloud, Gemini Enterprise, Workspace integrations. | Gemini API models, Google AI Studio, Google AI for developers. | Google’s AI family across chat, search-adjacent workflows, Workspace, Android, cloud, coding, and multimodal apps. |
| Perplexity | Perplexity answer engine, Pro Search, Sonar API, Search API, Agent API, Embeddings API. | Perplexity API, Sonar models, Perplexity. | An AI answer and research engine built around live web search, citations, and source-backed synthesis. |
| xAI Grok | Grok chat, Grok API, Grok Build, Grok Imagine, Grok Voice, X Search / web search tools. | xAI models, xAI API, Grok. | xAI’s model and product family, with emphasis on chat, reasoning, coding, real-time search, voice, image, and video. |
08 — Product types
Product Types: Chat, Code, Cowork, Search
| Product type | What it does | Examples |
|---|---|---|
| Chat Assistant | Answers questions, drafts text, analyzes files, brainstorms, summarizes, and helps with everyday knowledge work. | ChatGPT, Claude, Gemini, Grok. |
| Coding Agent | Reads and edits code, runs tests, fixes bugs, builds features, and can work across a software project. | OpenAI Codex, Claude Code, Grok Build, Gemini coding tools. |
| Cowork / Desktop Agent | Works on local files and multi-step knowledge tasks with more autonomy than a chat window. | Claude Cowork and similar desktop agents. |
| Search / Answer Engine | Searches current web sources and synthesizes an answer with citations. | Perplexity, Gemini with search grounding, Grok with web/X search, ChatGPT search. |
| API Platform | Lets developers build AI into apps, workflows, websites, and internal systems. | OpenAI API, Anthropic API, Gemini API, Perplexity API, xAI API. |
| Multimodal Studio | Creates or edits images, video, audio, slides, or interactive media. | Gemini media tools, OpenAI image/audio/video tools, Grok Imagine, Adobe/Canva AI features. |
09 — Compare
Quick Comparison
| Concept | Best for | Watchouts |
|---|---|---|
| Chatbot | Conversational Q&A and simple task support. | May give unsupported answers if not grounded. |
| RAG System | Answering questions from trusted internal or external documents. | Depends heavily on document quality and retrieval accuracy. |
| Fine-Tuned Model | Repeated specialized tasks with consistent style or domain rules. | Needs high-quality training data and ongoing evaluation. |
| AI Agent | Multi-step workflows that involve tools, decisions, and follow-through. | Needs permissions, guardrails, logging, and human review for important actions. |
| Predictive Model | Forecasting and classification from structured data. | Can fail when the future differs from historical patterns. |
10 — Prompting
Useful Prompt Formula
Use this structure for better AI outputs.
- Role: Tell the AI what perspective to take.
- Goal: State the outcome you want.
- Context: Provide relevant background, documents, data, or constraints.
- Format: Specify the output shape, such as bullets, table, memo, email, or checklist.
- Quality bar: Tell it what to optimize for, such as accuracy, brevity, citations, or executive readability.
Act as a finance analyst. Summarize this earnings call transcript for an investment committee. Focus on revenue drivers, margin pressure, capital allocation, guidance changes, and management tone. Output a one-page memo with key takeaways, risks, and follow-up questions.
11 — Start here
Executive Takeaway
The most important AI terms to understand first are LLM, prompt, context window, RAG, agent, MCP, API, CLI, hallucination, grounding, evaluation, and guardrails. Together, they explain how modern AI tools work, how they connect to real systems, where they are useful, and where they need oversight.
Provider/product examples and links were checked against public docs and product pages from OpenAI, Anthropic, Google, Perplexity, xAI, and the official Model Context Protocol documentation on July 1, 2026. Model names and product packaging change frequently, so use the links above for the latest details.