Glossary in plain words

1The whole idea in one picture

Now each word on its own.

2Token

A token is a small piece of text that a model reads and writes. It is not always a whole word: a long word gets split into several pieces. In English one word is about 1.3–1.5 tokens; in Russian it is about 2–3.

Analogy: a taxi charges by the kilometre, not by the trip. Models count tokens, not messages — and that is what you pay for.

Example: a 10-word English question is about 15 tokens, and a one-page answer (around 500 words) is about 700 tokens. Prices are quoted per 1 million tokens: with claude-haiku-5-5, a million output tokens costs $0.20.

3Model

A model is a specific AI that writes the answer: Claude Sonnet, GPT-6, DeepSeek, and so on. Each has a name (id) that goes into the model field of a request, for example claude-sonnet-5-5.

Analogy: models are like specialists in one office. One is quick and cheap, another takes longer but handles the hard cases.

The list and prices are in Models & pricing; tips for picking one are in How to choose a model.

4API

An API is the way one program talks to another. Instead of clicking buttons on a website, a program sends a request to an address and gets a response back.

Analogy: a takeaway counter. You pass in a note with your order and get the dish back. The Zukko API is that counter: your question and the model name go in, the model's answer comes out.

Example: POST https://api.zukko.pro/v1/chat/completions means "send a message to a model and get a reply".

5API key

An API key is a secret string that looks like fetch_…. It tells Zukko the request is yours and which balance to charge. You create it in Dashboard → API keys.

Analogy: a house key. Whoever holds it gets in — that is, spends your balance. So never post the key in code on GitHub, in chats, or in screenshots.

Example: programs keep the key in the ZUKKO_API_KEY environment variable rather than in the code itself. It is handy to give each bot or contractor its own key with a spend cap. If a key leaks, delete it in the dashboard and create a new one.

6Base URL

The base URL is the address a program sends its requests to. Many apps send requests to OpenAI or Anthropic by default; put the Zukko address into the base URL setting and they will go to Zukko instead.

Analogy: the address on an envelope. Same letter, different destination.

Both lead to the same Zukko; only the request format differs.

7Prompt

A prompt is the text you send to a model: a question, a task, or an example of what you want back.

Analogy: a brief for a contractor. The clearer the brief, the better the work.

Example: "write about coffee" is a weak prompt. "Write three sentences for a product page: whole-bean coffee from Colombia, smooth with notes of chocolate, for someone choosing a gift" is a strong one.

8System prompt

A system prompt is a standing instruction: "who you are and how to answer". It comes before the user's messages and applies to the whole conversation.

Analogy: a job description for a new hire. You hand it over once instead of repeating it before every customer.

Example: {"role": "system", "content": "You are a shoe store assistant. Keep answers short and only about our store."}. Keep a long, unchanging instruction at the start of the request — that is how the cache kicks in and makes it cheaper.

9Context

Context is everything the model sees in one request: the system prompt, the chat history, documents, and the question itself. Important: the model remembers nothing between requests. To make it "remember" a conversation, your program sends the history again every time.

Analogy: a consultant who rereads the client's entire file before every reply.

Example: most models have a 1-million-token context window — roughly 700,000 English words. Grok has 256K tokens, glm-5.3 has 128K. A longer context means more input tokens and a more expensive request.

10Cache

A cache is a saved result that doesn't have to be computed again. Zukko has two:

• Prompt cache. When the start of a request repeats (a long instruction, a knowledge base), the model reads it from the cache. With claude-sonnet-5 and claude-opus-5-5 that part costs 10 times less than regular input. It turns on by itself.
• Zukko response cache. Send exactly the same request again with the X-Zukko-Cache: true header and Zukko returns the saved answer for free.

Analogy: a cook who chopped the vegetables in the morning — the soup is faster and cheaper. More in Key limits, idempotency and caching.

11Streaming

Streaming means the answer arrives in pieces while the model is writing, instead of all at once at the end. That is how chats show text "typing" in front of you.

Analogy: a live broadcast instead of a recording shown in the evening.

Example: set "stream": true in the request. People see the beginning of the answer right away, and slow models don't hit connection timeouts. Note: if you close the stream halfway, the generated answer is still billed in full. More in Streaming.

12Agent

An agent is a model that doesn't just answer but completes a task step by step: it decides what to do, calls tools, looks at the result, and keeps going until it is done.

Analogy: an assistant with access to your calendar and mail. You say "book a meeting with Ivan on Thursday", and they check free slots and send the invite.

Example: Claude Code is an agent that reads project files, runs commands, and edits code. To build your own, see Agent with tools and Agent orchestration.

13Tools

Tools are functions your program offers to the model: "find an order by number", "get the exchange rate", "add a row to a spreadsheet". The model doesn't run them itself. It replies "call find_order with number 123"; your code runs the function and hands the result back to the model.

Analogy: a manager asks an assistant to look something up in the database, then decides what to do.

Example: in the request this is the tools field. Tools are verified to work with deepseek-v4.1-flash, claude-haiku-5-5, and gpt-6-luna.

14Pipeline

A pipeline is a conveyor belt: data passes through several steps automatically, often thousands of times in a row.

Analogy: a factory line where every part goes through the same machines.

Example: 10,000 reviews → the model detects mood and topic → JSON output → rows in a spreadsheet. Such jobs use cheap models and the Idempotency-Key header, so a retry after a failure is not charged twice. More in Pipelines.

15RAG

RAG (Retrieval-Augmented Generation) means "answering with a peek at the documents". Before asking, your program finds the relevant pieces of your documents and puts them into the prompt; the model answers from them.

Analogy: an open-book exam. The student doesn't remember everything but finds the right page quickly.

Example: a customer asks about delivery, so the bot finds three FAQ paragraphs about delivery and sends them to the model along with the question. Zukko has no embeddings, so the document search happens in your own code. More in Answers from your documents.

16JSON

JSON is a text data format that programs read easily: {"name": "Anna", "city": "Kazan"}.

Analogy: a form with fields instead of a free-form letter. A form is easy to split into spreadsheet columns.

Example: you can ask a model to reply strictly in JSON with the response_format parameter. deepseek-v4.1-flash and claude-haiku-5-5 are verified to follow a schema. Still, always check the JSON in your code and retry on errors. More in JSON output.

17Temperature

Temperature (temperature) controls how much randomness goes into the answers. At 0 the answers are predictable and nearly identical; at 0.7–1 they are more varied and "creative".

Analogy: a strict accountant versus a writer who loves to improvise.

Example: use a low temperature for data extraction and a higher one for ad slogans. Newer Claude models may reject a non-default temperature — if you get an error, just remove the parameter.

18Reasoning

Reasoning is a mode where the model "thinks" first: it writes a hidden draft, checks itself, and only then answers. It works better for math, code, and planning, but it is slower and costs more — the thinking is tokens too, and you pay for them.

Analogy: solving a problem on scrap paper before writing the clean copy.

Example: strong reasoning models (Kimi K3, Opus) can think for 30–60 seconds before the first word. Set your client timeout to 120–300 seconds and use streaming.

19What next