Dataset Generation

Generate training, evaluation, or fine-tuning samples from source material and generation rules, then save them in a defined structure.

Prepare your resources

Prepare verifiable source material, a sample purpose, an output schema, and a destination. Decide whether samples serve training, evaluation, or fine-tuning before defining fields.

Use the template

On the workflow creation page, start from a template, search for Dataset Generation, and load it onto the canvas.

Complete the required fields in the parameter form.

Setting

Purpose

Source material

Existing documents, business examples, requirements, or folders

Parsing tier

Controls parsing speed, quality, and cost

Dataset goal

Intended use of the dataset

Sample format

Type of sample to generate

Generation rules

Scope, quality requirements, difficulty distribution, and rules samples must follow

Output structure

JSON Schema for the generated dataset

Save location

Catalog location for the generated dataset

Save the parameters, then start a manual run. The system saves the current workflow before submitting execution.

Complete processing flow

Arrows show execution order; configured bindings supply each step’s input.

Dataset Generation

Configure generation

Setting

Default and use

Parsing tier

Standard; verify complete source text first

Dataset goal

training; also supports evaluation and fine_tuning

Sample format

qa_pairs; structured and instruction-tuning samples are also available

Sample count

20 requested samples; actual output may differ

Generation rules

Define scope, source constraints, missing content handling, and difficulty

Output schema

JSON Schema consistent with the format and instructions

Generation mode

n_to_1, combining source material

The default structure contains samples with instruction, input, output, source, and tags fields. When selecting question-answer pairs, still check the schema’s field design; selecting a format does not validate that design for you.

The saving step binds the single result. Switching to per-document n_to_n also requires handling multiple results and errors in saving. See Information Extraction.

Example: evaluation questions from product documentation

  1. Select a product document, choose evaluation as the goal, and choose question-answer samples.

  2. Specify chapter coverage, answers grounded only in the source, and source descriptions.

  3. Define an array with question, answer, and source fields in the schema. Request a small sample set.

  4. Keep combined generation, choose an output volume, and run.

  5. Verify that each question is answerable, the answer is correct, and the source supports it. Count accepted samples afterward.

Inspect saved results

Reading, parsing, generation, and saving run sequentially. The generation result is saved as generated-dataset.json. This template has no separate knowledge base indexing step.

Check actual counts, duplicates, structure, and sources. A nonempty generated source field is not proof of grounding; compare it with the original.

Common problems

Symptom

Check and action

Too few samples

Compare the result with source scope before adjusting the request

Answers add unsupported facts

Constrain sources in instructions and review samples

Unexpected fields

Align schema and instructions

Per-document output is incompletely saved

Check multiple-result bindings; the default saves one result

Data is needed for training

Review content, structure, duplicates, and sources, then convert for the training system
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