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 generation for AI training models

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.

Quick start

Base URL

Example request

{
  "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
}

Free preview for 5 messages. Sign up to continue.

Service notes

Pricing model: Usage-based pricing (~$5 per 1M tokens) billed on total tokens (input + output). See the API pricing page for current plans.
Data retention: No prompt/output retention by default. Operational telemetry (token counts, timestamps, error codes) is retained for billing and reliability.
Compatibility: OpenAI-style /v1/chat/completions request and response format with a base URL switch.
Latency: Depends on model size, prompt length, and load. Streaming reduces time-to-first-token.
Throughput: Team plans include priority throughput. Actual throughput varies with demand.
Rate limits: Limits vary by plan and load. Handle 429s with backoff and respect any Retry-After header.

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.

Instruction-tuning prompt/completion pairs for supervised fine-tuning.
Labeled classification, moderation, routing, or extraction examples.
Multi-turn conversation traces for agents, assistants, and copilots.
Edge cases, adversarial prompts, and red-team style evaluation sets.
Domain-specific examples for support, legal, healthcare, finance, and internal tooling.

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.

Include the fields your training or eval job actually consumes, such as input, ideal_output, label, difficulty, and metadata.
Keep label vocabularies stable so you do not create drift across batches.
Separate train, validation, and eval splits instead of generating one undifferentiated blob.

Example JSONL-style rows

{"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.

Name the task explicitly: classification, extraction, summarization, agent routing, or instruction tuning.
Set the desired class balance so the model does not overproduce the most common label.
Request diverse phrasing, lengths, user personas, and failure modes.

Reusable prompt template

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.

Run schema validation and reject malformed JSON before it reaches your dataset store.
Deduplicate near-identical rows so your model does not learn repeated phrasing.
Review samples manually for realism, label consistency, and policy compliance.
Benchmark on a holdout eval set to confirm the generated data improves quality instead of just adding volume.
Track the prompt template and generation settings used for each batch so runs are reproducible.

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.

No prompt/output retention by default. Requests are processed transiently, with only operational telemetry retained for billing and reliability.
Use Policy Gateway when you need policy-controlled generation, scoped keys, quotas, and audit logs for dataset jobs.
Generate privacy-safe variants or de-identified examples when you do not want raw production records in your prompts.

Common errors & fixes

401 Unauthorized: Check that your API key is set and sent as a Bearer token.
404 Not Found: Make sure the base URL ends with /v1 and you call /chat/completions.
400 Bad Request: Verify the model id and that messages are an array of { role, content } objects.
429 Rate limit: Back off and retry. Use the Retry-After header for pacing.