The most common words used in the context of AI and the Claude Code tool (a terminal-based AI programming assistant from Anthropic that can also be used for many other tasks) come mainly from English terminology in programming, machine learning and git workflows.
Claude Code is designed to generate, refactor and analyse code in the terminal or an app, so its key terms reflect everyday operations in AI-assisted coding.
Key terms
| Term / Status | Meaning in AI (Claude, Cursor, Copilot) | What actually happens “under the bonnet” | Etymology / Origin |
| Prompt | An instruction or request for the AI. | The model converts text into mathematical vectors for analysis. | From Latin promptus (ready). |
| Token | The basic unit of text (a piece of a word). | The unit you pay for; the AI “reads” in these pieces. | From Latin token (sign). |
| Thinking… (in orange) | The model’s internal reasoning phase. | The model uses “Chain of Thought” to plan its approach before it starts writing. | From English think. |
| Indexing… (in orange) | Indexing local files. | The tool (for example Cursor) maps your folder structure into a vector database. | From Latin index (pointer). |
| Context | The AI’s contextual memory. | The history of files and messages within the current attention “window”. | From Latin contextus (joined together). |
| Commit | Recording changes in a git repository. | Claude Code analyses the diff (changes) and suggests a version description. | From Latin committere (to entrust). |
| Refactoring | Rewriting code to improve its quality. | The AI removes duplication from the code while keeping the same behaviour. | From Latin factor (maker). |
| Applying… (in orange) | Applying changes to a file. | The AI physically rewrites lines of code on your disk. | From Latin applicare (to attach). |
| Reviewing (in orange) | Checking the proposed changes. | The model performs self-reflection, checking whether it has left any errors in the code. | From Latin re-videre (to see again). |
| Inference | The response generation phase. | The model “predicts” the most likely next token. | From Latin inferre (to infer). |
| Agent | An autonomous AI entity. | Claude Code launches the terminal on its own, reads errors and fixes them. | From Latin agens (acting). |
| RAG | Retrieval-Augmented Generation. | The process in which the AI “looks up” information in your documentation. | An English abbreviation (retrieving information). |
| Rate Limited (in orange) | The request limit has been exceeded. | The API has temporarily blocked you because your requests were too frequent. | From Latin rata (share) + limit. |
| Temperature | The AI’s creativity parameter. | A higher number means bolder but riskier code. | From Latin temperatura. |
| Hallucination | A factual error by the model. | The AI has statistically filled in nonsense that sounds plausible. | From Latin hallucinari (to wander). |
Use in practice
These terms appear in Claude Code in commands such as generate function, refactor commit or use context. For example, the claude commit command automatically creates a commit from the diff. The etymology is often Latin, which reflects the scientific roots of AI.
From a marketing perspective, the key thing to understand is that latency affects the user experience (UX) of chatbots, while a well-set temperature determines whether generated ad copy will be creative or dryly technical.
Why should you care? (A specialist’s view)
As a marketing strategist and SEO specialist, you can use these terms in content about AI tools, for example by optimising articles with keywords such as “prompt engineering” or “AI agenti pro byznys” (AI agents for business) for better visibility in Czech search results.
Our tip: If you are building a content strategy, do not focus only on the generic word “AI”. Look for overlaps. Companies today are not just searching for “how to write with AI”, but for how to automate their internal processes using the API and Claude Code. That is the real content gap waiting to be filled.
What is AI in simple terms
Artificial intelligence (AI) is software that learns patterns from large amounts of data. It uses those patterns to do tasks that normally need human judgement, such as writing text, recognising images or answering questions. It does not think or understand the way a person does. It calculates which answer is most likely to fit, based on what it has seen before.
In everyday language, you can picture AI as an extremely well-read assistant with no personal experience. It has read more text than any person could, so it can write fluent answers on almost any topic. It works with probabilities rather than checked facts, so it can also be confidently wrong. You still need to review what it produces.
How AI works, explained step by step
- Collecting data. Developers gather a large set of examples, such as text, images or code.
- Training. The model processes the examples many times and adjusts its internal settings until its predictions become accurate.
- Testing and adjusting. The developers check the results, correct unwanted behaviour and often train the model further for specific tasks.
- Using the model. When you type a prompt, the model applies what it learned and builds a response one small piece at a time.
Most of the learning happens before you ever use the tool. A chatbot does not learn from your conversation the way a person does. It only works with what you give it in the current context.
More AI terms worth knowing
- Machine learning is a branch of AI in which systems learn from data instead of following rules written by hand.
- Neural network is a structure of connected mathematical units, loosely inspired by the brain, that most modern AI is built on.
- Large language model (LLM) is a neural network trained on huge amounts of text to understand and generate language. Claude, GPT and Gemini are examples.
- Parameters are the internal values a model adjusts during training.
- Fine-tuning means training an existing model further on a smaller, specific dataset, such as your company’s support tickets.
- Latency is the time between sending a request and receiving the answer.
- API is an interface that lets your own software send requests to an AI model and receive the answers automatically.
- Multimodal describes a model that can work with more than one type of input, such as text, images and audio.
Common mistakes when learning AI terms
- Treating AI and machine learning as the same thing. Machine learning is one approach within the wider field of AI.
- Assuming the model knows current events. It only knows its training data, unless the tool connects it to live sources or your documents.
- Confusing tokens with words. A single word can be split into several tokens, so length limits and costs do not match the word count.
- Using jargon with clients or colleagues. If you cannot explain a term in one plain sentence, you probably do not need it in the conversation.
Frequently asked questions about AI terms
What is AI in simple terms?
AI is software that learns patterns from large amounts of data and uses them to perform tasks such as writing, translating or recognising images. It does not understand things like a person does. It predicts the most likely answer based on what it learned during training.
How can you explain AI in layman terms?
Think of AI as a very well-read assistant that has never lived in the real world. It can write and answer questions fluently because it has processed enormous amounts of text. However, it can also make convincing mistakes, so you should always check its work.
Is AI the same as machine learning?
No. AI is the broad goal of making machines perform tasks that normally need human intelligence. Machine learning is one way of reaching that goal, in which the system learns from examples instead of following hand-written rules. Most modern AI tools rely on machine learning.
Do you need to know technical AI terms to use AI tools?
You can use most AI tools without any technical knowledge. Knowing a few basic terms, such as prompt, token and context, helps you get better results and understand the tool’s limits. It also makes it easier to compare tools and talk to developers.
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