> ## Documentation Index > Fetch the complete documentation index at: https://docs.platform.toloka.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Glossary > Shared Platform terms, Data Labeling vocabulary, and Train concepts. Each term is marked **Core**, **Data Labeling**, or **Train**. A **pipeline run** processes data in Data Labeling; a **Training Run** trains a model in Train. **Model** belongs to Train; a pipeline uses a **generation model** or **review model**. ## Agent **Data Labeling** The built-in assistant you build pipelines with, in a chat on the project page. You describe the task in plain language. The agent assembles the pipeline, writes the instructions and the labeling screen, and sets quality control, audience, and pricing. See [How the platform works](https://docs.platform.toloka.ai/docs/start/how-it-works.md). ## API key **Core** A credential you generate in the Business Console. It acts as your user for the HTTP API, and for the CLI when automation cannot sign in through a browser. Access follows the teams you belong to. See [CLI](https://docs.platform.toloka.ai/docs/cli.md) and [Programmatic access (API)](https://docs.platform.toloka.ai/docs/api-reference.md). ## Audience **Data Labeling** The set of experts eligible to label on a Generation or Quorum node, filtered by language, specialization, and country. It applies to expert labeling only. Synthetic labeling has no audience. See [Generation node](https://docs.platform.toloka.ai/docs/data/reference/nodes/generation.md). ## Audience reuse **Data Labeling** Reusing the expert audience from a deployed Generation or Quorum node, typically one in another pipeline of the same project, so experts who passed that node's onboarding start work without repeating it. It is unavailable under synthetic labeling. See [Generation node](https://docs.platform.toloka.ai/docs/data/reference/nodes/generation.md). ## Balance **Core** The prepaid funds owned by your team and shared by its projects. Pipeline runs draw down the balance as they label and pause when it no longer covers the work. A Training Run reserves its maximum charge, then settles GPU usage against that hold. The products have different settlement rules. See [Billing](https://docs.platform.toloka.ai/docs/core/billing.md). ## Base model **Train** A catalog model you can train from, identified by a stable id such as `qwen3-8b`. A Training names either one base model or one existing Model Version as its source. See [Fine-tune a model](https://docs.platform.toloka.ai/docs/train/guides/fine-tune.md). ## Batch **Data Labeling** The items one run processes, up to the 10,000-item per-run limit. A large or recurring workload is a cycle of batches: deploy one, clone the pipeline, point the copy at the next, deploy again. See [Label a large dataset in batches](https://docs.platform.toloka.ai/docs/data/guides/label-at-volume.md). ## Branch **Data Labeling** One of a Match node's outgoing paths. Each condition is a branch, labeled by the condition's name. The default branch carries every item that matches no condition. See [Match node](https://docs.platform.toloka.ai/docs/data/reference/nodes/match.md). ## Business Console **Core** The separate Toloka application where money enters your account: **Top up** and **Billing history** open its billing page in a new tab. The platform reads your balance from it and links to it. Adding funds, invoices, and payment details live there. ## Check **Data Labeling** A test or lint on one node. Test checks use sample cases: **Code Test**, **Annotation Quality Assurance**, and **Synthetic Generation**. Built-in lints start automatically after saved edits and inspect settings or selected data. Required lints must finish before deployment. See [Self-check](https://docs.platform.toloka.ai/docs/data/concepts/self-check.md). ## Checkout **Core** A directory bound to one platform project by a `toloka.json` file. The CLI treats that file as the project root. See [CLI](https://docs.platform.toloka.ai/docs/cli.md). ## Clone **Data Labeling** A copy of a pipeline that carries the same configuration into a new pipeline in the same project. Because a deployed pipeline is locked, cloning is how you label another batch or start a new version. See [Label a large dataset in batches](https://docs.platform.toloka.ai/docs/data/guides/label-at-volume.md). ## Condition **Data Labeling** A named group of field tests on a Match node. Each test compares one input field's value, and the condition passes when all of its tests pass, or any one, as configured. Each condition gets its own branch. An item that matches no condition follows the default branch. See [Match node](https://docs.platform.toloka.ai/docs/data/reference/nodes/match.md). ## Contract **Data Labeling** The per-item prices for one run, frozen when you deploy and unchanged for the life of that run. Each run freezes its own contract, so a clone does not inherit the original's prices. The run's **Contract** button reopens it at any time. See [Pricing and billing](https://docs.platform.toloka.ai/docs/data/concepts/pricing.md). ## Cost breakdown **Data Labeling** The **Cost breakdown (forecast)** window shows the forecast before you deploy, with one row per billable line and an **Expected** and a **Maximum** total for the run. It lists the real per-task, per-call, and per-token prices and computes the totals. See [Pricing and billing](https://docs.platform.toloka.ai/docs/data/concepts/pricing.md). ## Cost estimate **Train** A free check of a Training's selected rows that returns either validation problems or a maximum charge. The Training reads a frozen Dataset Slice. A ready estimate expires 24 hours after you request it. See [Training](https://docs.platform.toloka.ai/docs/train/concepts/how-training-works.md#from-draft-to-model-version). ## Data collection **Data Labeling** A task type where experts produce new content. Uploading media is one example. Experts do not label data you already have in this task type. Its labeling interface must include a media-upload control, it is the one task type that may use the wider unverified expert pool, and synthetic labeling is not available on it. See [Generation node](https://docs.platform.toloka.ai/docs/data/reference/nodes/generation.md). ## Dataset **Core** A project-owned collection of items and fields. Data Labeling reads and writes fields through a pipeline; Train reads conversation examples from a frozen [Dataset Slice](#dataset-slice) and learns from selected train-role parts of its [split column](#split-column). See [Create and manage datasets](https://docs.platform.toloka.ai/docs/core/datasets.md) and [Prepare training data from labels](https://docs.platform.toloka.ai/docs/train/guides/prepare-training-data.md). ## Dataset Slice **Core** A frozen selection of dataset rows and fields at a cutoff time. A Training uses a ready slice that includes a split column. The CLI's `--dataset` option fills the dataset's split column, creating an 80/10/10 train, validate, and test column when it has none, and freezes a slice; `--slice` selects a ready one. See [Slices and split columns](https://docs.platform.toloka.ai/docs/train/reference/settings.md#slices-and-split-columns). ## Deploy **Data Labeling** Launching a run: you confirm the cost, the contract freezes, and the run begins labeling. Deploying locks the pipeline, so you cannot edit or rerun it and a further batch needs a clone. A deploy that failed is the exception: it frees the same pipeline to fix and deploy again. See [Label a large dataset in batches](https://docs.platform.toloka.ai/docs/data/guides/label-at-volume.md). ## Exam **Data Labeling** A short test on real tasks that experts on a Generation or Quorum node must pass before they get production tasks. Turn it on with **Enable Exam** on the node's **Exam** card, choose a golden dataset whose rows are the exam tasks, choose how each output field is graded, and set **Tasks per Expert** and **Pass threshold**. Each expert who passes is charged once, as **Exam (one-time per expert)**. Synthetic labeling has no exam. See [Exam](https://docs.platform.toloka.ai/docs/data/reference/nodes/generation.md#exam). ## Experts **Data Labeling** The people who label your data. Pages also call them expert labelers. You choose the audience and set the per-task price. The agent writes the instructions and the labeling screen they see. See [How the platform works](https://docs.platform.toloka.ai/docs/start/how-it-works.md). ## Export **Data Labeling** A downloadable JSON file of a run's results, with one entry per item carrying its inputs and the output fields the pipeline produced. Each export is a snapshot taken when you start it. Download links expire; click the download icon in the export list to generate a fresh link. See [Export format](https://docs.platform.toloka.ai/docs/data/reference/export-format.md) for the file's contract and [Export your labeled results](https://docs.platform.toloka.ai/docs/data/guides/export-results.md) for the steps. ## Field **Core** A named value on an item. Input fields are the values a node reads. Output fields are the values it writes. Each field has a name and a type. See [Fields and data](https://docs.platform.toloka.ai/docs/data/concepts/fields-and-data.md). ## Generation model **Data Labeling** The model that produces the labels under synthetic labeling, chosen per node from the platform's catalog with a recommended default. It is separate from the [review model](#review-model), which checks those labels. See [Label with an LLM instead of experts](https://docs.platform.toloka.ai/docs/data/guides/synthetic-labeling.md). ## Gist tokens **Train** The number of learned soft tokens used to represent the prompt. Set an absolute count (`8`, `32`, or `64`) or a fraction of the prompt (`"1/4"`, `"1/8"`, `"1/16"`, or `"1/32"`). The default is `"1/8"`. See [Training settings](https://docs.platform.toloka.ai/docs/train/reference/settings.md#gist-tokens). ## Gisting **Train** A Training method that learns a compact prompt representation from example conversations plus a prompt text file. It is not supervised fine-tuning. See [Training](https://docs.platform.toloka.ai/docs/train/concepts/how-training-works.md#two-methods). ## Human review **Data Labeling** A review path where expert reviewers check a sample of a node's labeled items, 10 to 100 percent of them. It is available on the Generation node only. The Quorum node and synthetic labeling do not offer it. See [Quality control](https://docs.platform.toloka.ai/docs/data/concepts/quality-control.md). ## Item **Core** One entry in a dataset, carrying its field values. A pipeline run processes a selection of items, up to 10,000 per run. Train has separate [selection limits](https://docs.platform.toloka.ai/docs/train/reference/limits.md). See [Fields and data](https://docs.platform.toloka.ai/docs/data/concepts/fields-and-data.md). ## Item filter **Data Labeling** The Start node's filter that narrows which dataset items a run launches. It matches by a field's value, by the source that recorded a value, or by a field's absence, and it evaluates as of the moment the run launches. See [Start and End nodes](https://docs.platform.toloka.ai/docs/data/reference/nodes/start-and-end.md). ## Labeling interface **Data Labeling** The screen an expert fills in for one item. The agent builds the form from your task. It shows the input fields and collects a value for each output field. On the node it is the **UI for Experts** card, where **View** previews it. Use **Annotate with UI** in **Annotation Quality Assurance** to test a submission. Pages also call it the labeling screen. See [Generation node](https://docs.platform.toloka.ai/docs/data/reference/nodes/generation.md). ## LLM review **Data Labeling** A review path where an LLM checks every labeled item against your quality criteria and returns a verdict for each. It is the default review path and always covers every item, with no sampling. See [Quality control](https://docs.platform.toloka.ai/docs/data/concepts/quality-control.md). ## Location **Core** How a part of a structured value is addressed: it starts at the value's root, written `$`, and steps inward one named member or list position at a time. Match node tests, item filters, repeating-field declarations, and derived fields each use one. See [Fields and data](https://docs.platform.toloka.ai/docs/data/concepts/fields-and-data.md). ## LoRA rank **Train** A supervised fine-tuning setting: the rank of the LoRA adapter, `8`, `16`, or `32`, with `16` the default. A larger rank gives the adapter more capacity and needs more data. See [Training settings](https://docs.platform.toloka.ai/docs/train/reference/settings.md#lora-rank). ## Maximum charge **Train** The most a Training Run can cost: the price of the estimate's maximum GPU seconds, rounded up to the cent. The platform places a hold for this amount on the team balance when the run starts and charges only the GPU seconds used by a run that completes or that you cancel; a run that fails because of the platform is not charged. See [Training](https://docs.platform.toloka.ai/docs/train/concepts/cost.md). ## Model **Train** A project-owned named container for immutable Model Versions produced by Training Runs. The first Training Run creates the Model from the Training name. See [Fine-tune a model](https://docs.platform.toloka.ai/docs/train/guides/fine-tune.md). ## Model Version **Train** One immutable published result of a completed Training Run, with a unique name and a server-assigned revision. See [Fine-tune a model](https://docs.platform.toloka.ai/docs/train/guides/fine-tune.md). ## Node **Data Labeling** One unit of a pipeline. A pipeline is a connected set of nodes, each of a small, fixed set of kinds that label, transform, or route your data. See [Pipeline nodes](https://docs.platform.toloka.ai/docs/data/reference/nodes.md). ## Offboarding rule **Data Labeling** A Quorum rule on the **Offboarding** card, required in **No QA, Offboarding Only** mode. A **Majority-vote agreement** rule picks one [single-label output field](#single-label-output-field) and offboards experts who persistently disagree with the majority on it. It does not grade, reject, or change answers. The mode also requires an overlap of at least 3 and the per-expert cap off. See [Offboarding rules](https://docs.platform.toloka.ai/docs/data/reference/nodes/quorum.md#offboarding-rules). ## Onboarding **Data Labeling** A one-time course experts complete before they label items on a Generation or Quorum node. On the node it is the **Training** card, and its charge is **Onboarding (one-time per expert)**, or **Training (one-time per expert)** when an [exam](#exam) is enabled. It is unrelated to a Train [Training](#training). It needs three to six steps and at least one quiz with correct answers. It can also include reading material, consent, practice exercises, or worked examples. Practice exercises and worked examples cannot appear in the same course. See [Generation node](https://docs.platform.toloka.ai/docs/data/reference/nodes/generation.md). ## Overlap **Data Labeling** The number of experts who independently label each item on a Quorum node — the card calls it the number of independent annotations per task. It is a whole number from 1 to 100. Raising it raises the expert cost in step. See [Quorum node](https://docs.platform.toloka.ai/docs/data/reference/nodes/quorum.md). ## Per-expert cap **Data Labeling** An optional limit on how many tasks a single expert may take on a node, a whole number from 1 to 50. It is off by default. See [Generation node](https://docs.platform.toloka.ai/docs/data/reference/nodes/generation.md). ## Per-task price **Data Labeling** The price you set for one labeling task on a Generation or Quorum node. **Expert Service Cost** in the cost breakdown shows your charge: the per-task price plus the platform commission. On Quorum, this charge scales with the number of experts per item. See [Pricing](https://docs.platform.toloka.ai/docs/data/reference/nodes/generation.md#pricing). ## Pipeline **Data Labeling** The connected set of nodes that takes your data in and produces labeled results. The agent assembles it from your task description. You review it, launch it, and export from it. See [How the platform works](https://docs.platform.toloka.ai/docs/start/how-it-works.md). ## Project **Core** The workspace unit that holds your pipelines, datasets, secrets, Trainings, and Models. You create one by describing a task to the agent, or with `toloka project create`. Every pipeline and Training belongs to one project. See [Get your first labeled dataset](https://docs.platform.toloka.ai/docs/data/get-started/first-labeled-dataset.md), [Fine-tune a model](https://docs.platform.toloka.ai/docs/train/guides/fine-tune.md), and [CLI](https://docs.platform.toloka.ai/docs/cli.md). ## Quality criterion **Data Labeling** A named check a labeled output is judged against, with a pass condition, a fail condition, and an optional instruction for the LLM reviewer, targeting one output field. Reviewers apply the criteria and return a verdict for each. See [Quality control](https://docs.platform.toloka.ai/docs/data/concepts/quality-control.md). ## Reasoning effort **Data Labeling** A synthetic-labeling setting for how hard the generation model reasons on each item: Low, Medium, or High on models that support it, with Medium the default. Higher effort raises the estimated tokens per item and the price. Lower effort lowers both. See [Generation node](https://docs.platform.toloka.ai/docs/data/reference/nodes/generation.md). ## Repeating field **Data Labeling** An output field that can hold several values within a single item. It returns a list rather than one value. On a Quorum node, a repeating output comes back as one list per expert. See [Fields and data](https://docs.platform.toloka.ai/docs/data/concepts/fields-and-data.md). ## Review metadata field **Data Labeling** An extra output field a reviewed node adds alongside your own fields, named after the node and ending in `_qa_metadata`, with details about how the item's labels were reviewed. See [Quality control](https://docs.platform.toloka.ai/docs/data/concepts/quality-control.md). ## Review model **Data Labeling** The model that performs LLM review on a node. It is set per node, separately from the generation model under synthetic labeling, and each model has its own per-call price. See [Quality control](https://docs.platform.toloka.ai/docs/data/concepts/quality-control.md). ## Review path **Data Labeling** How a node's labels are reviewed: LLM review, human review, both, or none, depending on the node. The agent turns quality control on by default. See [Quality control](https://docs.platform.toloka.ai/docs/data/concepts/quality-control.md). ## Run **Data Labeling** One execution of a deployed pipeline over a batch of items. A run is created at deploy and does not run again. More data means more runs. See [Label a large dataset in batches](https://docs.platform.toloka.ai/docs/data/guides/label-at-volume.md). ## Secret **Core** A credential stored in your project, such as an API key, that a Code node's environment variable can point to, so the value stays out of your code. You create one on the project's **Secrets** page. The agent can reference a secret by name but cannot create one. See [Code node](https://docs.platform.toloka.ai/docs/data/reference/nodes/code.md). ## Self-check **Data Labeling** Validation, lints, and sample tests used before deployment. Configuration errors and failed checks block deployment. Unrun test checks are warnings you can override. Required lints must finish: Quality Requirements and User Interface Types on labeling nodes, Dataset Items on Start, and Exam Golden Dataset on expert-labeling nodes with an enabled [exam](#exam). See [Self-check](https://docs.platform.toloka.ai/docs/data/concepts/self-check.md). ## Single-label output field **Data Labeling** An output field where each expert picks exactly one label from a fixed set. On a Quorum node, agreement is measured on single-label output fields: each [offboarding rule](#offboarding-rule) selects one. See [Quorum node](https://docs.platform.toloka.ai/docs/data/reference/nodes/quorum.md). ## Source **Core** Who recorded a field's value: a person or an automated source such as an earlier pipeline run. A field can hold several recorded values over an item's life, each with its own source, and a node reads the latest by default. See [Fields and data](https://docs.platform.toloka.ai/docs/data/concepts/fields-and-data.md). ## Specialization **Data Labeling** A skill area used to filter a node's audience, alongside language and country. Specializations require [verified experts](#verified-experts) and are not available with the unverified pool. See [Generation node](https://docs.platform.toloka.ai/docs/data/reference/nodes/generation.md). ## Split column **Core** A dataset column that assigns rows to named parts with `train`, `validate`, or `test` roles. A Training uses a split column frozen in its Dataset Slice and learns from all train-role parts by default. Use `--split-column` to choose the column and `--parts` to select particular train parts. See [Slices and split columns](https://docs.platform.toloka.ai/docs/train/reference/settings.md#slices-and-split-columns). ## Step **Data Labeling** A page of an expert onboarding course. In the Items tab, **Current Step** identifies the [node](#node) an item has reached. See [Generation node](https://docs.platform.toloka.ai/docs/data/reference/nodes/generation.md). ## Synthetic labeling **Data Labeling** Having an LLM produce a node's labels instead of expert labelers, with each label checked against your quality criteria by a separate LLM call. Quality control is LLM review only, and the expert audience, pricing, per-expert cap, and onboarding do not apply. See [Label with an LLM instead of experts](https://docs.platform.toloka.ai/docs/data/guides/synthetic-labeling.md). ## Task **Data Labeling** One unit of expert work: one item shown to one expert to label or review. Pricing is per task. On a Quorum node, one item produces as many tasks as it has experts, and the per-expert cap counts tasks. See [Generation node](https://docs.platform.toloka.ai/docs/data/reference/nodes/generation.md). ## Team **Core** The account unit that owns projects and the balance. Pipeline runs and Training Runs draw on the same team's balance. See [Teams, access, and balance](https://docs.platform.toloka.ai/docs/core/teams-and-balance.md). ## toloka **Core** The command-line program shipped with the platform SDK. You sign in through your browser or with an API key, select a project, reconcile pipeline source with `plan` and `apply`, and run Training commands. See [CLI](https://docs.platform.toloka.ai/docs/cli.md). ## Training **Train** An editable draft that names a method (supervised fine-tuning or gisting), a source model, and a frozen Dataset Slice with a split column. Training learns from the selected train-role parts; `--parts` narrows that selection. The CLI can prepare a slice with `--dataset` or use a ready slice with `--slice`. Updating a Training does not change an existing Training Run. See [Training](https://docs.platform.toloka.ai/docs/train/concepts/how-training-works.md). ## Training Run **Train** One billed execution of a Training from a ready cost estimate. It is immutable. A completed run publishes one Model Version. See [Fine-tune a model](https://docs.platform.toloka.ai/docs/train/guides/fine-tune.md). ## Verdict **Data Labeling** The result of checking one quality criterion on one labeled output: pass or fail, each with a written reason. Besides pass and fail, a verdict can have the state skipped or an error state when the check could not run for that item. A criterion on a repeating output returns a list of verdicts, one per value. See [Quality control](https://docs.platform.toloka.ai/docs/data/concepts/quality-control.md). ## Verified experts **Data Labeling** Experts who have completed ID verification. Labeling tasks require verified experts, and specializations are available only with them. Only [data-collection](#data-collection) tasks may use the wider unverified pool. See [Generation node](https://docs.platform.toloka.ai/docs/data/reference/nodes/generation.md). This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.