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Orchestrate Fluency Evaluation Workflow

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: AI Fluency Index Evaluator with LangGraph and OpenAI o4-mini

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Linked challenge
AI Fluency Index Evaluator with LangGraph and OpenAI o4-mini

Prompt source

Original prompt text with formatting preserved for inspection.

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Design the LangGraph workflow: User input goes to `InteractionAgent`, then to `BehaviorAnalyst`, whose output informs the `FluencyCoach`. The `FluencyCoach`'s feedback should then be presented to the user, and their response fed back into the `InteractionAgent` to form a continuous evaluation and coaching loop. Include conditional edges for adaptive branching based on user feedback or identified behaviors.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.