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Challenge

Responsible AI Claims Communications Filter with Sentiment Guardrails

Deploying customer-facing AI in claims management requires rigorous empathy and accuracy safeguards. Build an AI SDK sentiment and policy guardrail system that monitors customer communications in real-time, preventing tone-deaf or legally binding commitment errors.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop a real-time guardrail system using AI SDK to filter customer-facing AI claims communications.

Delivery guide

How work is evaluated

Evaluation

Evaluates guardrail intervention precision on policy-binding and empathy tests.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

safe_response_non_empty

Ensures safe response text is provided when draft fails

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

guardrail_precision

Precision in identifying unauthorized commitments • target: 0.98 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Implement Vercel AI SDK middleware for streaming response interception

  • Detect tone and empathy scores in automated claim status updates

  • Flag unintentional coverage promises prior to customer delivery

  • Measure filter latency impact on streaming user experiences

Resources and assets

Reference links and supporting material

Dataset notes

Dataset of customer claim queries and AI response drafts with safety labels.

Challenge setup

Evaluates guardrail intervention precision on policy-binding and empathy tests.

Challenge input

JSON containing customer message and AI draft response.

AI SDK

Fast client and server text processing primitives

Evaluated output

JSON containing pass_guardrail boolean, flagged_reason, and safe_response_text.

Evaluation checks
  • Ensures safe response text is provided when draft fails
  • Precision in identifying unauthorized commitments • target: 0.98 • range: 0-1
Configured evaluation evidence
  • Benchmark: ResponsibleCommBench
  • Guardrail Precision target: 0.98
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Aim · Required
  • RAI · Optional
  • Ray Serve · Optional

Evaluation contract

  • safe_response_non_empty · Weight 1
  • guardrail_precision · Weight 1

Metrics

  • guardrail_precision · Target 0.98

Benchmarks

  • ResponsibleCommBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing customer message and AI draft response.

    Output: JSON containing pass_guardrail boolean, flagged_reason, and safe_response_text.

    Execution harness: JavaScript

Evaluation tools

  • AI SDK · TypeScript AI SDK

Recipe freeze marker

No recipe freeze time is recorded.

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