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MLflow Evaluation Pipeline Design

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

Linked challenge: Financial Proxy Analyst AI

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Linked challenge
Financial Proxy Analyst AI

Prompt source

Original prompt text with formatting preserved for inspection.

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Outline the design of an MLflow evaluation pipeline for your Proxy Analyst AI. How will you log LLM prompts, responses, and metrics (e.g., ROUGE, custom validity scores)? Describe the steps to compare AI-generated summaries and recommendations against a 'golden' dataset using MLflow's tracking and logging capabilities.

Adaptation plan

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

Keep stable

Preserve the rubric, target behavior, and pass-fail criteria as the baseline for evaluation.

Tune next

Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.

Verify after

Make sure the prompt catches regressions instead of just mirroring the happy-path examples.