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Convergence Loop

Definición

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.

Términos relacionados

Flujos de trabajo de IA

Conozca qué son los flujos de trabajo de IA y cómo automatizan procesos de múltiples pasos. Los flujos de trabajo encadenan recetas con ramificaciones, bucles, puertas de aprobación y ejecución paralela.

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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