Synthetic training data is training or evaluation data generated by software, usually another AI model, instead of collected directly from production events. It helps when the target is checkable: a label, schema, tool call, redaction decision, code fix, or policy outcome. Its main risk is false confidence. If the examples are cleaner than real user behavior, a model can learn the generator’s style instead of the business decision it is supposed to make.<\/p>\n\n\n\n
Production data can be sensitive, expensive to label, sparse in rare cases, or legally hard to move across regions. Synthetic data can be shaped around the cases a team wants to teach, then routed through validation, review, and evaluation before it reaches training. The question is not whether synthetic data is real enough. The question is whether it improves a routed model, eval suite, or fine-tune without importing the habits of the generator model.<\/p>\n\n\n\n
Provider batch APIs matter only because they shape the workflow. OpenAI, Anthropic, Google Vertex AI, Azure OpenAI, and Amazon Bedrock all document batch or offline inference surfaces that can support large synthetic-data jobs.[1][2][3][4][5] Those docs should be treated as operational constraints: what file format is accepted, how failures are returned, whether output can be audited after completion, and whether the job window fits the review process. Raw pricing, limits, and expiration windows belong in a maintained comparison page or appendix, not in the main explanation.<\/p>\n\n\n\n
Synthetic data helps most when the task has a checkable target. Good candidates include support-ticket intent labels, JSON extraction from invoices, function-calling arguments, PII redaction test cases, short code repair examples, and adversarial user phrasings for a policy classifier. It is weaker when the target is subjective, changing, or hard to verify, such as \u201cmake this answer sound trustworthy\u201d without a rubric.<\/p>\n\n\n\n
For structured tool examples, the source of truth should be the schema, not the generator’s prose. OpenAI’s function-calling documentation and Anthropic’s tool-use documentation both emphasize named tools and structured inputs.[6][7] A useful synthetic record should therefore include the user message, expected tool name, expected arguments, validator result, and reviewer decision.<\/p>\n\n\n\n
Public benchmarks can help with the first model shortlist, but they should not decide whether your synthetic data worked. MMLU and GPQA are useful signals for broad knowledge and reasoning; SWE-bench Verified and HumanEval are more relevant when code behavior matters; preference leaderboards such as LMArena can reveal user-perceived answer quality.[8][9][10][11][12] None of them replace a task-specific eval on your own real holdout set.<\/p>\n\n\n\n
The main failure is not that synthetic examples are \u201cfake.\u201d The failure is that they can be too clean. A generator may produce tidy user requests, consistent grammar, complete facts, and polite tone, while production users submit fragments, screenshots, abbreviations, typos, copied email threads, and conflicting instructions. A classifier trained mainly on polished synthetic examples can learn the generator’s style instead of the business decision.<\/p>\n\n\n\n
The second failure is circular evaluation. If the same model family generates the examples, labels them, and judges the trained model, the workflow can look good while adding little real ability. Keep three datasets separate: generator input, reviewed training data, and a real holdout set that the generator did not create or label. If performance rises on synthetic tests but stays flat on real tickets, the data is teaching the wrong pattern.<\/p>\n\n\n\n
The third failure is provider mismatch. A batch job is useful for offline labeling, eval generation, and nightly audits, but it is not a replacement for a synchronous endpoint inside a user-facing flow. Provider docs are still important, because completion windows, queueing behavior, file constraints, and cache behavior decide whether a synthetic-data run can be inspected before it becomes stale. They should not distract from the core question: can the output be validated and measured against real examples?<\/p>\n\n\n\n
Consider a support-ticket classifier that routes billing, account access, abuse, refunds, and general product questions. The real dataset has thousands of ordinary billing tickets but only a small number of refund disputes after annual renewals. The model routes common tickets well, yet it misses the rare refund class when users write emotionally, paste invoice fragments, or mention cancellation and renewal in the same message.<\/p>\n\n\n\n
The team does not generate a new full dataset. It generates only candidate records for the named gap: renewal refund disputes with messy phrasing, partial context, copied email snippets, and ambiguous cancellation language. Automatic checks remove invalid labels, duplicate phrasings, and examples that contain fake personal data. Reviewers reject examples that sound too complete or that make the refund decision obvious from artificial wording.<\/p>\n\n\n\n
The first run fails in a useful way. The generator keeps writing \u201cI would like a refund for my annual subscription,\u201d which is too clean compared with real tickets. The prompt is revised to require fragments, contradictions, and realistic support context. After review, only edited or accepted records enter training. On the frozen real holdout set, the rare refund slice improves, common billing labels do not regress, and synthetic-only test gains are ignored unless the real slice also moves.<\/p>\n\n\n\n
The strongest synthetic-data workflow starts with a written spec that a reviewer can apply consistently. Define the task, allowed output format, rejected output format, edge cases, source fields, and pass\/fail criteria. For extraction tasks, require schema validation. For classification tasks, require a label guide with examples. For code tasks, require executable tests. For policy tasks, require a reviewer note explaining why the label is correct.<\/p>\n\n\n\n
A concrete workflow for a support-ticket classifier looks like this.<\/p>\n\n\n\n
Track provenance on every record. Minimum fields should include source_type, generator_provider, generator_model_family, prompt_version, schema_version, created_at, reviewer_id, review_status, and eval_split. Those fields let you answer the question that always appears later: \u201cWhich prompt or model started producing bad labels?\u201d<\/p>\n\n\n\nDecision<\/th> Use this rule<\/th><\/tr><\/thead> Generate more data?<\/td> Only generate for a named gap in the label guide, not for the whole dataset by default.<\/td><\/tr> Accept synthetic labels automatically?<\/td> Accept automatically only when a deterministic validator can prove the output shape and value range; otherwise require review.<\/td><\/tr> Use batch?<\/td> Use batch when the job is offline, auditable, and can finish inside the provider’s documented window.<\/td><\/tr> Use synthetic evals?<\/td> Use them for regression coverage, but never as the only launch metric.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n How model selection fits in<\/h2>\n\n\n\n