How to Use the OpenAI Batch API Job Planner Prompt to Label Thousands of Records Without a Fragile Loop

When you need a language model to classify or extract fields from a large dataset, looping over the regular API one record at a time is slow, easy to interrupt, and painful to resume. The Batch API is built for this kind of offline work: you upload a file of requests, the job runs within a completion window, and you download the results. The hard parts are the details. Every line needs a stable identifier, results do not come back in input order, and failed requests need their own retry path. The OpenAI Batch API Job Planner: JSONL Request Files with custom_id, Structured Output Schemas, Status Polling, and Result Joins for Bulk Classification prompt plans the whole job, from the JSON schema for your answers to the script that joins results back to your source rows.

What the prompt produces

1. A job design that checks whether batch processing fits your deadline and how to split your data into files within the current limits.
2. A strict JSON schema for the answer, with required fields, label enums, and no extra properties allowed.
3. A complete example JSONL line with custom_id, method, url, and a body that includes the model, your instructions, the record, and the schema.
4. Script steps to build and validate the file, upload it with purpose batch, create the batch with a 24h completion window, and poll its status.
5. A result join that downloads the output and error files, matches each line by custom_id, parses the answer, and writes it to your database.
6. A retry plan for failed or expired requests and a human spot check before you trust the labels.

How to fill the inputs

TaskSpec is one sentence describing the job plus the labels or fields you want. Clear label definitions lead to a cleaner schema and fewer arguments later about edge cases.

SourceRows describes the data: file type, row count, primary key, and two sample rows. The primary key becomes the basis for every custom_id, which is what lets you match results reliably.

ModelEndpoint names the model and endpoint your team already tested. All lines in one batch file should target the same model and endpoint.

CurrentPrompt is the system message and examples that work today on single calls. The prompt reuses them rather than inventing new instructions.

Runtime sets the language for the scripts. Python with the official SDK, Node, or plain command line requests all work.

ResultStore tells the prompt where results go and when they are needed, so it can judge whether a batch job fits your timeline.

Reading the example output

The example classifies a large set of support tickets into five categories with an urgency level:

• The schema is strict. Categories and urgency are enums, all fields are required, and additional properties are not allowed, so parsing is predictable.
• custom_id comes from the ticket ID. Each line carries a prefix plus the ticket number, which makes the join a simple string operation.
• The scripts are split by job. One builds and validates files, one submits, and one polls, which makes reruns safer.
• The API key stays out of files. The client reads it from an environment variable.
• Failures are kept. Raw text is stored when parsing fails, and a retry file is built from only the failed identifiers.
• A person checks the labels. A random sample is reviewed by a support lead before the labels feed any dashboard.

Tips for better results

• Run a small batch first with a few hundred records. It catches schema and prompt problems before you spend on the full set.
• Validate every line locally before uploading. A single malformed line can cause validation trouble for the whole file.
• Save the batch ID and file IDs as soon as you create them, so you can resume polling if your script stops.
• Check the official documentation for current pricing, limits, and turnaround guidance instead of relying on old blog posts.

Mistakes to avoid

• Do not assume output order matches input order. Always join by custom_id.
• Do not reuse custom_id values across rows, even by accident from duplicate source keys.
• Do not ignore the error file. It often holds the records you most need to look at.
• Do not put API keys in notebooks or JSONL files that get shared.

Who it is for

Data and ML engineers running bulk classification or extraction, analysts labeling feedback or support data, and product teams that want structured fields pulled from large text archives on a schedule.

Related PromptDig links

Start with the OpenAI Batch API Job Planner: JSONL Request Files with custom_id, Structured Output Schemas, Status Polling, and Result Joins for Bulk Classification prompt and describe your task and data shape. To find more prompts for AI workflows, Browse more prompts. If you have a production ready AI tooling prompt, Share a prompt.