Prompt decomposition — breaking a complex task into smaller sub-prompts that are easier for the model to handle reliably.
Map-reduce prompt pattern — processing chunks of data in parallel (map), then synthesising the results in a final prompt (reduce).
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A pipeline feeds the summary from step 1 into step 2's prompt. This is an example of:
Prompt chaining connects LLM calls so that the output of one becomes the input of the next. This is fundamental to building reliable LLM pipelines: rather than asking a model to do everything in one prompt (which increases failure risk), you break the work into steps and pass structured information between them.
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Your team processes a 200-page document by splitting it into 20 chunks, summarising each with a separate LLM call, then combining the summaries. This is the:
The map-reduce prompt pattern mirrors the classic MapReduce algorithm. In the map phase, each chunk is processed independently (and in parallel) with the same prompt. In the reduce phase, the partial outputs are synthesised by a final prompt. This is essential for handling documents longer than the model's context window.
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A prompt DAG represents a prompt pipeline as a directed acyclic graph. What does this structure enable?
A prompt DAG (directed acyclic graph) models the prompt pipeline as nodes (LLM calls or tool calls) connected by directed edges (data flow). Because it's acyclic, it has no infinite loops. The DAG structure allows orchestrators like LangGraph or Prefect to execute independent branches in parallel and merge results at join nodes, making complex pipelines both efficient and predictable.
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In a prompt chain, what is a handoff?
A handoff is the transfer of control and state between steps in a prompt chain or between agents in a multi-agent system. A well-designed handoff includes: the output of the previous step, any relevant context, and clear instructions for what the next step should do. Poor handoffs (e.g., passing unstructured text) are a common source of pipeline failures.
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Why is prompt decomposition recommended for complex tasks?
Prompt decomposition improves reliability because LLMs perform better on narrow, well-defined tasks than on vague, multi-faceted ones. A single prompt asking a model to "analyse, summarise, classify, and translate" in one go concentrates many potential failure points. Breaking this into four sequential prompts, each doing one thing well, dramatically reduces error rates and makes debugging easier.