Batch Jobs for Large LLM Workloads
Run thousands of model requests as batch jobs that are cheaper, resumable and easy to check.
A taste of a lesson
My batch finished but results are in a different order. How do I know which is which?
That is why each request line needs a custom id. Batch results usually come back with the id you sent, and you should never rely on line order. Load the results into a dict keyed by custom id, then loop over your original items and look each one up. Any item with no result, or with an error, goes on a retry list for a new batch. If you did not set ids this time, add them before the next run. What did you use as ids?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Decide between a provider batch API, your own queue and live calls
- Prepare request files with stable ids and validate them before submitting
- Run a pilot and spot check quality before the full job
- Collect, match and verify results and retry only failed items
- Estimate batch job cost and track it against actual usage
Lesson plan
- 1 Is this a batch job? Decide whether a workload suits batch processing, a queue or live calls. Start
- 2 Building request files Create one valid request per item with a stable, unique id. Start
- 3 Pilot first Run a small sample and check quality before spending on the full job. Start
- 4 Submitting and monitoring Split work, submit batches and track their status sensibly. Start
- 5 Results, failures and reruns Match results by id, record errors and retry only what failed. Start
- 6 Quality and cost checks Verify the finished job and compare actual cost to the estimate. Start
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About this tutor
For developers who need to classify an archive, generate descriptions for a catalogue, run a large evaluation or create embeddings for a whole document set. You learn when a provider batch API fits (work that can wait hours, often at a discount) and when to build your own queue instead, how to prepare request files with stable ids, validate them before submitting, monitor jobs, collect results, retry failures and match outputs back to inputs. The tutor covers size limits, splitting work, cost estimation and quality checks on samples, keeping the steps general because batch interfaces differ between providers.
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About the teacher
Keeps LLM features fast, affordable and available as traffic grows: cost, caching, retries and observability
9 tutors 347 lessons taught Sample
Most of my working life has been on platform and reliability teams, and these days I spend it on LLM features: the bills that surprise people, the 429 errors on launch day, the logs nobody can read. I teach the operational side of building with model APIs. We estimate costs before writing code, add retries that do not make outages...
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