AI Workflow Patterns

AI Workflow Patterns

Fact-check status

Reviewed claim by claim on 2026-09-02 against the primary or official sources identified inline as S1 and S2. Recommendations are design guidance unless a cited standard makes them normative.

Key jargon

Term Plain-language meaning
Prompt chain A sequence where one model output becomes a later step's input. S1
Routing Classifying a request and sending it to a specialized path. S1
Parallelization Running independent subtasks concurrently. S1
Evaluator-optimizer A loop in which one component critiques and another revises against a stopping rule. S1

Key concepts

Concept map

flowchart LR
    A["Decompose job"] --> B["Select chain route or parallel pattern"]
    B --> C["Evaluate intermediate artifacts"]
    C --> D["Join and deliver"]

Diagram semantics verified against S1 and S2 on 2026-09-02.

Pattern Use Main risk
Prompt chain Decompose staged transformations Early errors propagate S1
Router Select specialist/model/tool Misrouting and hidden fallback S1
Parallel fan-out Independent research or candidate generation Duplicate work and costly synthesis S1
Evaluator–optimizer Draft and revise against a rubric Evaluator shares the same blind spots S1
Orchestrator–workers Dynamic task decomposition Unbounded work and conflicting outputs S1
Map–reduce Process many documents then aggregate Lost cross-document relationships S1
Human gate Approve risky transition Approval fatigue or vague preview S1

Design method

For each node define input schema, output schema, owner, model/tool, timeout, retry rule, evidence, and transition condition. S1

Dumpster lesson

The prompt-chaining research intake found a useful principle: structured intermediate artifacts make failures localizable. Treat the paper’s reported scores as author claims, not universal proof. S1

Exercise

Design a three-stage research digest: cluster evidence, synthesize each cluster, then reconcile claims. Specify how a failed cluster is represented.