implementation

Initial Mastra AI Setup

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Linked challenge: Health-LLM Rubric Evaluator with Mastra AI and Biologically-Inspired Forgetting

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Linked challenge

Health-LLM Rubric Evaluator with Mastra AI and Biologically-Inspired Forgetting

Prompt source

Original prompt text with formatting preserved for inspection.

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1 sections

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Initialize a Mastra AI project. Create an agent named 'HealthEvaluator' that has access to a toolset for 'EmpathyCheck', 'SafetyCheck', and 'FactCheck'. Use TypeScript to define the agent's base memory class with a placeholder for the FadeMem decay logic.

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.

Prompt diagnostics

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Purpose

implementation

This prompt already mixes executable detail with instructions, so tune examples and interfaces before rewriting the scaffold.

Linked challenge

Health-LLM Rubric Evaluator with Mastra AI and Biologically-Inspired Forgetting

Leveraging the Health-SCORE rubric and FadeMem research, this challenge requires building a scalable evaluation agent using Mastra AI. You will create a workflow that assesses Health-LLM responses across multiple attributes (e.g., empathy, factual correctness, safety) and visualizes the results in a Retool dashboard. Crucially, you must implement the 'FadeMem' concept within the Mastra AI memory system. As the evaluator processes thousands of logs, it should implement a decay function to 'forget' or compress low-impact evaluation data while retaining critical safety violations and high-quality rubric examples. This ensures the evaluation agent remains efficient and avoids memory bloat during large-scale clinical trials.

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Problem Definition and Requirements Gathering

To establish a clear understanding of the problem space and define comprehensive requirements for the AI solution.