AI batch processing groups asynchronous model requests into a managed job. It can suit evaluations, classification, and other work that does not require an immediate interactive answer. A batch’s lifecycle is not the same as one synchronous call, and a completed job does not automatically mean every requested item succeeded.
A reliable workflow preserves request identity and reconciles each outcome. This guide explains input validation, result mapping, partial failure, and retention so efficiency gains do not create missing records or duplicate consequential actions.
Select workloads that can wait
Choose work with an acceptable asynchronous completion window. Offline labeling or an evaluation run can often tolerate delay; an urgent user interaction may not. Make the business deadline explicit before selecting a batch interface.
Check the provider’s current supported endpoints, models, limits, and completion windows. These details can change. Avoid promising one timing or discount as a permanent property of every AI batch system.
Separate generated results from external side effects. A delayed model output should not automatically perform an irreversible action merely because it arrived successfully. Keep validation and required human approval at the application boundary.
Build an authoritative input manifest
Give every request a unique nonsecret identifier and retain the mapping to the intended source record. The identifier should support reconciliation without exposing personal data in filenames or broad provider metadata.
Validate each request’s structure before upload. Check model, endpoint, required fields, output limits, and supported parameters. One invalid input file can prevent a job from entering normal processing.
Record the input version, prompt configuration, and evaluation or business purpose. A later retry should use an intentional configuration rather than silently mixing new prompt behavior into the same supposedly comparable run.
Protect uploaded files and private data
Batch inputs can contain many requests in one artifact, concentrating sensitive information. Apply the approved data-sharing policy before upload. Do not send private records to a provider simply because the job is operationally convenient.
Minimize fields and redact values not needed for the task. Keep input artifacts in controlled storage and avoid copying them into broad logs or issue trackers. An upload identifier can be logged without exposing the whole file.
Review provider retention and deletion behavior for files and outputs. The application’s own storage also needs a retention policy. Deleting a local copy does not necessarily delete every remote artifact created by the workflow.
Observe the full job lifecycle
Distinguish validation, queued or active processing, finalization, completion, cancellation, and expiration according to the provider’s interface. A job identifier proves submission, not acceptance or successful processing.
Poll with an appropriate interval and bounded operational deadline. Avoid rapid status loops that add unnecessary load or cost. Persist job identity so a worker restart can resume observation rather than submitting a duplicate batch.
Assign an owner for stuck or failed jobs. A terminal error should produce a controlled response and useful evidence. Repeatedly recreating the same invalid job without inspecting its failure is not recovery.
Match results by identity rather than line order
Output order may differ from input order. For example, OpenAI’s documented batch workflow includes custom_id values for mapping results back to requests. Use the supported identity field instead of positional matching.
Verify every output identifier belongs to the manifest and appears according to the expected uniqueness rules. Unexpected or duplicate identifiers should not overwrite another record silently. Keep the reconciliation result auditable.
Account for the expected set of requests, not only the returned success lines. A job with ninety valid results out of one hundred needs ten explicitly classified outcomes, not a report that calls the batch universally successful.
Validate every successful-looking result
Check response status, output schema, allowed values, and task-specific correctness before accepting a result. A successful model request can still return unusable or incorrect business content.
Apply the same authorization and tenant checks used by the synchronous workflow. Matching a request identifier does not grant permission to update an arbitrary record. Resolve the identifier through the protected manifest and application context.
Keep uncertain or rejected outputs separate from accepted records. Human review may be required for consequential classifications. Do not let bulk processing remove the quality gate that existed for individual requests.
Retry only the unresolved work
Read both success and error artifacts where the provider supplies them. Expired or cancelled jobs can still have completed results. Reconcile those results before deciding which requests need another attempt.
Create a new retry manifest containing only approved unresolved items. Keep links to the original request and attempt count. Bound retries and distinguish transient failure from persistent invalid input or policy refusal.
Make acceptance idempotent at the application level. If a result file is downloaded twice or a worker crashes after writing one result, the next reconciliation should not duplicate an external action or insert the same accepted outcome twice.
Test retention, cancellation, and recovery
Test a small representative batch, malformed input, partial errors, cancellation, expiration behavior where practical, and worker restart. Verify the manifest’s final state rather than only the provider’s top-level job status.
Retrieve and preserve required output before documented provider deletion deadlines. A completed job whose results later expire may no longer be recoverable through the original interface. Keep only what the approved retention policy requires.
For a classification run, persist the input manifest, map output by request ID, validate each label, and report accepted, failed, and pending counts separately. The batch then improves throughput without obscuring completeness or authority.
Track reconciliation as a separate job
Downloading a result file is not the same as applying every accepted result. Persist reconciliation progress so an interruption can resume safely without skipping earlier records or duplicating writes.
Use bounded processing and record per-item acceptance status. A large file may need controlled streaming rather than loading everything into memory. Keep the provider job state and application reconciliation state separate so operators can distinguish completed generation from completed business processing.
Frequently asked questions
Does completed mean every request succeeded?
Not necessarily. Reconcile success and error outcomes for the full input manifest.
Can I match output lines by input position?
Do not assume order is preserved. Use the provider’s supported unique request identifier.
Where can I check a concrete batch lifecycle?
Read the OpenAI Batch API guide for its current limits, statuses, files, and mapping requirements.
For a complementary workflow, read AI Structured Outputs: 7 Checks for Reliable Application Data.
