Convergence Loop
Définition
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.
Termes associés
Workflows IA
Découvrez ce que sont les workflows IA et comment ils automatisent les processus multi-étapes. Les workflows enchaînent des recettes avec des branchements, des boucles, des portes d'approbation et l'exécution parallèle.
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.
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DAG (Directed Acyclic Graph) execution runs workflow steps concurrently based on their dependency graph, enabling parallel processing of independent tasks.
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