Batch Optimization: Refine Many Prompts at Once
Batch optimization sends 2 to 20 prompts through the identical four-step pipeline in one submission, billed at the reduced Flex inference rate in exchange for a longer processing window. Rows are independent — a failed row is refunded automatically and does not stop the others — but safety is all-or-nothing: one prompt tripping the threat scanner rejects the whole batch before any work starts.
Refining prompts one at a time does not scale. Batch optimization sends 2 to 20 prompts through the same pipeline in a single submission, bills them at a lower Flex rate, and gives you one progress view that handles partial failures and refunds for you.
By Andrei Bădulescu, Founder at VantagePrompt·Updated
Optimizing one prompt at a time is fine when you have one prompt. When you have twenty, it is a chore. Batch optimization runs a whole set of prompts through the same pipeline in a single submission, then gives you one progress view to watch them finish.
Every prompt in a batch goes through the exact same four-step pipeline as a single optimization: intent classification, template selection, framework-aware generation, and a quality score. You are not trading quality for throughput. You are just running the pipeline in parallel instead of babysitting one tab.
When should I batch instead of using the single optimizer?
Batch when you have volume and can wait; use the single optimizer when you want one prompt polished now. Both run the identical pipeline, so the choice trades latency for cost, never quality.
- You have a backlog of rough prompts to clean up in one pass (a prompt library, a set of agent instructions, a column from a spreadsheet).
- You want to A/B several phrasings of the same idea and compare quality scores side by side.
- You are seeding a template or marketplace entry and need consistent, optimized variants.
- Cost matters more than latency — batches are billed at a lower rate (more on that below).
| Batch optimization | Single optimizer | |
|---|---|---|
| Prompts per run | 2 to 20 | One |
| Billing mode | Flex inference — reduced rate for a longer processing window | Standard rate |
| Feedback | Progress bar plus per-row status | Output streams token by token |
| Best for | Volume: backlogs, A/B phrasings, template variants | The one prompt you need polished right now |
| Failure handling | Per-row: one failure does not stop the rest | The single run either succeeds or fails |
If you only have one prompt, or you want to watch the optimized output stream token-by-token, use the single optimizer instead. Batch is built for volume, and its UX is a progress bar, not a live stream.
How many prompts fit in one batch?
A batch needs at least 2 prompts and accepts at most 20. Each prompt follows the same length rules as a single optimization (a handful of characters minimum, up to 3000). If you have more than 20, split them into multiple batches client-side.
On the batch page you enter one prompt per blank line in the input pane, pick a model from the curated list, and dispatch. Here is what the input looks like for a small batch of marketing prompts:
write a launch tweet for our new API rate-limit dashboard
draft a changelog entry announcing SSO support
turn these release notes into a short LinkedIn post for engineers
write a cold email intro to a CTO about our observability toolBlank lines separate prompts. Four prompts above means four rows in the batch, each optimized independently with its own quality score.
Why is a batch cheaper than running prompts one by one?
Batches run on Flex inference by default. Flex is a batch-pricing mode that trades a longer allowed processing window for a reduced rate — it is enabled for batch jobs and is not used for single-prompt optimization. That is the core trade: you accept that the set takes minutes rather than seconds, and in exchange the whole run costs less per prompt than running each one individually through the single optimizer.
If you have a pile of non-urgent prompts to refine, batching them is the cheaper path. Save the single optimizer for the one prompt you need polished right now.
How do I track a batch while it runs?
Once dispatched, a batch shows a live progress panel: an overall progress bar plus a per-row status icon. Each row moves through pending, processing, then completed or failed. The view polls for updates on a relaxed cadence (batches run for minutes, so there is no need to hammer the server every few hundred milliseconds), and it stops polling automatically once every row has finished.
The header keeps a running count of how many rows completed, how many failed, and how many credits were refunded. Expand any completed row to see its optimized output and quality score. Recent batches stay listed on the page so you can come back to a run later — you do not have to keep the tab open while it processes.
What happens when one prompt in the batch fails?
A batch does not fail as a unit when one prompt has trouble. Rows are independent: if one prompt hits a transient error or a per-row quota issue, that single row is marked failed with an error message while the rest keep going. You get the successful results without re-running everything.
Credits are handled per row. The whole batch is checked and charged up front, and when a row fails for a runtime reason its credit is returned to you automatically. You will see that refund in three places: the batch progress header counter, a per-row note on the batch detail page, and your credit ledger as a refund entry. There is one exception — a prompt blocked for adversarial or safety reasons forfeits its credit and is not refunded.
Safety is all-or-nothing at submission. If any single prompt in the batch trips the threat scanner, the entire batch is rejected before any work starts — adversarial input cannot piggyback on legitimate prompts. Review your inputs before dispatching a large set.
How do I export the results?
There is no separate batch export. Every completed row writes a normal entry to your history, so the standard history export covers the same data — optimized output, model used, and quality score for each prompt. Filter your history to the batch's timeframe and export in whatever format you need.
What does a practical batch workflow look like?
- Collect your rough prompts — keep each batch to 20 or fewer.
- Paste them one per blank line, pick a model, and dispatch.
- Let the progress panel run; leave the page if you want, the batch keeps processing.
- Review quality scores per row; expand the weak ones to read the optimized output.
- Re-batch any failed or low-scoring rows after tweaking the input.
- Export the keepers from your history.
For picking the right model and routing behavior, see Model Selection & Smart Routing. If you find yourself batching the same prompt shape with different inputs over and over, a reusable template with variables will save you the copy-paste — see Reusable Templates with Variables.
Frequently asked questions
- How many prompts can one batch hold?
- At least 2 and at most 20. Each prompt follows the same length rules as a single optimization, up to 3000 characters. Split larger sets into multiple batches. On the batch page you enter one prompt per blank line.
- Is batch output lower quality than the single optimizer?
- No. Every prompt in a batch goes through the exact same four-step pipeline — intent classification, template selection, framework-aware generation, and a quality score. You are running the pipeline in parallel, not trading quality for throughput.
- Why is a batch cheaper than running each prompt individually?
- Batches run on Flex inference by default, a batch-pricing mode that trades a longer allowed processing window for a reduced rate. It is enabled for batch jobs and not used for single-prompt optimization, so the set takes minutes rather than seconds and costs less per prompt.
- What happens if one prompt in my batch fails?
- Rows are independent: that row is marked failed with an error message while the rest keep going, and its credit is returned automatically. You will see the refund in the progress header counter, a per-row note, and your credit ledger. The one exception is a prompt blocked for adversarial or safety reasons, which forfeits its credit.
- Why was my whole batch rejected before it started?
- Safety is all-or-nothing at submission. If any single prompt trips the threat scanner, the entire batch is rejected before any work begins, so adversarial input cannot piggyback on legitimate prompts. Review your inputs before dispatching a large set.
- How do I export batch results?
- There is no separate batch export. Every completed row writes a normal entry to your history, so the standard history export covers the same data — optimized output, model used, and quality score. Filter history to the batch's timeframe and export from there.
Sources
Put it into practice.
Run this technique in the optimizer.