Map-Reduce Prompting Patterns: Processing Large Data with AI
By Learnia Team
Map-Reduce Prompting: Processing Large Data with AI
This article is written in English. Our training modules are available in multiple languages.
What happens when your input is too large for a single context window? Or when you need to process 500 documents with the same prompt? Map-Reduce is the answer — a pattern borrowed from distributed computing that splits work into parallelizable chunks, processes each independently, and merges the results.
The Map-Reduce Pattern
Use Case: Document Summarization
Error Handling in Map-Reduce
Advanced: Cascading Map-Reduce
Test Your Understanding
Next Steps
You now command the full prompt orchestration toolkit: chaining, routing, and Map-Reduce. In the next module, you will learn RAG (Retrieval-Augmented Generation) — the technique that gives AI access to YOUR data by combining retrieval with generation.
- →Agent Architecture Patterns — The Map-Reduce pattern in the context of agent architectures
Continue to RAG Fundamentals to build AI systems grounded in your own data.
Module 4 — Chaining & Routing
Build multi-step prompt workflows with conditional logic.
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FAQ
What will I learn in this Prompt Orchestration guide?+
Learn the Map-Reduce pattern for AI: split large inputs into chunks, process in parallel, and merge results. Covers document summarization, data analysis, and batch processing.