Glossary · Phase 1 · Building with LLMs
Prompt chaining
Splitting a job into steps, where each model call's answer feeds the next call.
Try it now
A fake model stands in for the real one, so you can see the shape of a chain. Each step's return value becomes the next step's argument. The final check runs in plain Python. The last line prints: email(facts(rain shell)) True
Learn it properly
Prompt chaining · Break big tasks into a pipeline of smaller model calls. About 30 minutes.
Other terms from the same lesson
- map-reduce: Splitting a big job into pieces, handling each piece separately (map), then combining the results (reduce).
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