Do you support synthetic data generation for training models?

Yes. You can use abliteration.ai to generate synthetic training data, fine-tuning pairs, evaluation sets, labeled datasets, and edge-case examples through the OpenAI-compatible API.

Can I generate JSONL-ready examples for fine-tuning?

Yes. Prompt the model to return a fixed schema and convert the response into JSONL rows for your training pipeline. Common fields include input, ideal_output, label, difficulty, and metadata.

Can I govern synthetic-data jobs with policy rules?

Yes. Policy Gateway can sit in front of generation workloads to apply policy-as-code rules, scoped keys, quotas, and audit logging for dataset creation workflows.

Will prompts used for synthetic data generation be stored or used for training?

No. Prompts and outputs are not retained by default and are never used to train or fine-tune models. Only operational telemetry such as token counts, timestamps, and error codes is retained for billing and reliability.

Guides

Synthetic data API for LLM evals

Generate synthetic training data, LLM eval rows, classifier examples, synthetic datasets, and labeled datasets with an OpenAI-compatible API.

Updated 2026-08-04

Use abliteration.ai to generate synthetic data for training models, fine-tuning runs, evaluations, and edge-case coverage with the same OpenAI-compatible API you use for live inference.

Create prompt-completion pairs, labeled classification rows, multi-turn conversations, and structured JSON or JSONL-ready outputs without changing your client stack.

{
  "model": "abliterated-model",
  "messages": [
    {
      "role": "system",
      "content": "You generate high-quality synthetic training data. Return strict JSON only."
    },
    {
      "role": "user",
      "content": "Create 5 synthetic instruction-tuning examples for a customer support assistant. For each item include input, ideal_output, label, difficulty, and rationale."
    }
  ],
  "temperature": 0.8
}

What you can generate

Synthetic data generation is useful when you need more coverage than your real datasets provide or when you want privacy-safe seed data for training and evaluation.

How to structure outputs for training pipelines

Ask the model for a fixed schema so downstream validation is simple. JSON arrays work well for smaller jobs, while JSONL-style rows are easier to stream into training pipelines.

{"input":"Customer asks for a refund after 45 days","ideal_output":"Explain policy and offer eligible alternatives.","label":"refund_policy","difficulty":"medium"}
{"input":"User reports duplicate charges on an invoice","ideal_output":"Request billing details and outline the verification process.","label":"billing_dispute","difficulty":"easy"}

Prompt template for higher-quality synthetic data

Synthetic datasets get better when you specify task boundaries, class balance, edge conditions, and output format in the prompt.

You are generating synthetic data for model training.

Task: Create 50 examples for [task name].
Audience/domain: [industry or workflow].
Required labels: [label_1, label_2, ...].
Output schema: JSON array with fields input, ideal_output, label, difficulty, metadata.
Constraints:
- Make examples realistic, diverse, and non-duplicative.
- Cover common cases, rare cases, and edge cases.
- Do not include placeholders.
- Keep outputs self-contained and ready for review.

Quality checks before you train

Synthetic data should be treated like any other training asset: validate it, sample it, and measure whether it improves the downstream task.

Privacy, governance, and auditability

Synthetic data generation often starts from sensitive workflows. Keep those prompts private and add governance when the generation pipeline needs controls.