Batch LLM processing means sending many records to an AI model in a controlled workflow.

The input is often a CSV file, Excel file, CRM export, lead list, product catalog, or scraped dataset. Each row becomes one AI task. The result is written back to the dataset.

This is useful when one prompt works for thousands of rows:

  • Score 20,000 companies
  • Classify 8,000 support messages
  • Translate 12,000 product descriptions with AI translation at scale
  • Extract fields from scraped pages
  • Summarize one website per lead

Row processing versus a provider Batch API

“Batch LLM processing” can describe two workflows: running one prompt separately for many spreadsheet rows, or submitting an asynchronous job to a provider's dedicated Batch API. These have different execution and result-handling requirements.

Datablist's LLM enrichments run row-based model requests and map the results back to your items. Using them on a large selection does not mean you are submitting a job to OpenAI's Batch API. If you build a provider Batch API workflow yourself, keep an identifier for each request so returned results can be matched to their source rows.

In either workflow, keep a stable row identifier, the original input, and separate output columns. For example, classify a Product Description into Category and Reason, while preserving Product ID. This lets you inspect failed or uncertain results without losing the data needed to rerun them.

Why batch processing is harder than manual prompting

Manual prompting hides operational problems. Batch processing exposes them.

When you run AI on many rows, you need to handle:

  • API rate limits
  • Model timeouts
  • Failed rows
  • Retry logic
  • Token costs
  • Long inputs
  • Output formatting errors
  • Partial results
  • Audit and review

A small prompt can work in a chat window and still fail at scale.

⚠️ Do not skip sampling

Run the prompt on a small group first. Check wrong answers, empty answers, and output format before running on the full file.

A good batch LLM workflow

Start with a small and clear dataset.

Then:

  1. Pick the columns the model needs.
  2. Write the prompt with prompt variables.
  3. Define the output fields.
  4. Test on 10 to 50 rows.
  5. Fix the prompt and missing data rules.
  6. Run the full batch.
  7. Filter failed rows and rerun only those rows.
  8. Review a sample of completed rows.

This flow reduces cost and avoids replacing one manual task with another manual cleanup.

Retry and failed-row handling

Retries matter because AI providers can return temporary errors. A bulk workflow should not stop forever because one row failed.

Useful status values include:

  • Pending
  • Running
  • Completed
  • Failed
  • Skipped

Failed rows should remain visible. Separate temporary provider failures from input problems: retry a timeout, but correct missing text or an invalid credential before trying again. In Datablist, an entirely empty set of prompt-variable values produces an empty-input error rather than a model request. Check the run errors and rerun only the affected rows after fixing their cause. Review completed output separately for incorrect answers; a successful request does not prove the answer is correct.

Batch LLM processing in Datablist

Datablist turns LLMs into spreadsheet operations. You can import a CSV, write one prompt, map row fields with variables, and save answers into columns.

Use:

For row-by-row examples, see LLM spreadsheet processing, AI classification, and AI data extraction.