Splitting one impossible prompt into several reliable ones
When a single prompt tries to do four things it does all four adequately. Chaining trades latency and cost for accuracy you can measure per step.
Category: AI WorkflowsDifficulty: IntermediateVersion: 1.0Updated: April 5, 2025Author: Sabir
Topologies
Shape
Structure
Use when
Sequential
A → B → C
Each step genuinely needs the last one's output
Parallel
A and B → merge
Independent sub-questions; one round trip of latency
Conditional
Classify, then branch
Inputs of clearly different kinds
Map-reduce
Per-chunk, then combine
Input larger than the window
Iterative
Draft → critique → revise
Quality matters more than latency; cap the loop
Where to split
Where the output format changes — Extraction and prose want different temperatures
Where you want to validate — You can only check a step whose output has a shape
Where a cheaper model would do — Classification rarely needs your most expensive model
Where a human might need to approve — A checkpoint must sit on a boundary
NOT where it merely feels tidier — Every split adds a call, a failure mode and a place to lose context
Errors compound
Five steps at 95% each is 77% end to end, and the last step cannot tell that its input was already wrong. Validate between steps rather than only at the end, and pass the ORIGINAL input alongside intermediate results so a later step can notice the drift.
FAQs
When is one prompt better than a chain?
Whenever the task genuinely is one task. Every split adds a call, a failure mode and a place to lose context, so a chain has to earn its complexity in measured accuracy.
How do I stop errors compounding?
Validate between steps rather than only at the end, and pass the ORIGINAL input alongside intermediate results so a later step can notice the drift.