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
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:
- Pick the columns the model needs.
- Write the prompt with prompt variables.
- Define the output fields.
- Test on 10 to 50 rows.
- Fix the prompt and missing data rules.
- Run the full batch.
- Filter failed rows and rerun only those rows.
- 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. You can filter them, adjust the prompt, clean the input, and rerun the same enrichment only on those rows.
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:
- Ask ChatGPT/OpenAI for general text processing
- Ask Claude AI for longer text and writing tasks
- Ask Gemini for Google AI workflows
- Ask Mistral AI or Ask DeepSeek AI for other model options
- AI Agent when the row needs web search or page browsing
For row-by-row examples, see LLM spreadsheet processing, AI classification, and AI data extraction.