Convergence Loop
Definition
A convergence loop is a quality control mechanism in AI workflows that links an eval step (quality gate) back to an upstream step. When the eval scores output below a configurable quality threshold, the workflow automatically re-executes the upstream steps with feedback from the eval, iterating until the output meets the quality bar or a maximum iteration count is reached.
How It Works
Add an EvalStep after any recipe or LLM step and enable convergence. Set a quality threshold (e.g., 80/100) and maximum iterations (1-10). If the eval scores below threshold, it feeds its critique back into the upstream step's next iteration as context, enabling self-correction. This creates a refinement loop that produces higher-quality outputs without human intervention.
Verwandte Begriffe
KI-Workflows
Erfahren Sie, was KI-Workflows sind und wie sie mehrstufige Prozesse automatisieren. Workflows verketten Rezepte mit Verzweigungen, Schleifen, Genehmigungsschritten und paralleler Ausführung.
AI Bakeoff
An AI Bakeoff is a structured comparison that evaluates multiple LLM models or prompt variations on the same inputs using automated judge scoring.
DAG Execution
DAG (Directed Acyclic Graph) execution runs workflow steps concurrently based on their dependency graph, enabling parallel processing of independent tasks.
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