frank-luongt/skills/codex/databricks-mlflow-evaluation

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name: databricks-mlflow-evaluation

MLflow 3 GenAI Evaluation

Before Writing Any Code

1. Read GOTCHAS.md - 15+ common mistakes that cause failures
2. Read CRITICAL-interfaces.md - Exact API signatures and data schemas

End-to-End Workflows

Follow these workflows based on your goal. Each step indicates which reference files to read.

Workflow 1: First-Time Evaluation Setup

For users new to MLflow GenAI evaluation or setting up evaluation for a new agent.

| Step | Action | Reference Files | | ---- | --------------------------- | ---------------------------------------------------------------- | | 1 | Understand what to evaluate | user-journeys.md (Journey 0: Strategy) | | 2 | Learn API patterns | GOTCHAS.md + CRITICAL-interfaces.md | | 3 | Build initial dataset | patterns-datasets.md (Patterns 1-4) | | 4 | Choose/create scorers | patterns-scorers.md + CRITICAL-interfaces.md (built-in list) | | 5 | Run evaluation | patterns-evaluation.md (Patterns 1-3) |

Workflow 2: Production Trace -> Evaluation Dataset

For building evaluation datasets from production traces.

| Step | Action | Reference Files | | ---- | ----------------------------- | ------------------------------------------------ | | 1 | Search and filter traces | patterns-trace-analysis.md (MCP tools section) | | 2 | Analyze trace quality | patterns-trace-analysis.md (Patterns 1-7) | | 3 | Tag traces for inclusion | patterns-datasets.md (Patterns 16-17) | | 4 | Build dataset from traces | patterns-datasets.md (Patterns 6-7) | | 5 | Add expectations/ground truth | patterns-datasets.md (Pattern 2) |

Workflow 3: Performance Optimization

For debugging slow or expensive agent execution.

| Step | Action | Reference Files | | ---- | ----------------------------- | ---------------------------------------------------- | | 1 | Profile latency by span | patterns-trace-analysis.md (Patterns 4-6) | | 2 | Analyze token usage | patterns-trace-analysis.md (Pattern 9) | | 3 | Detect context issues | patterns-context-optimization.md (Section 5) | | 4 | Apply optimizations | patterns-context-optimization.md (Sections 1-4, 6) | | 5 | Re-evaluate to measure impact | patterns-evaluation.md (Pattern 6-7) |

Workflow 4: Regression Detection

For comparing agent versions and finding regressions.

| Step | Action | Reference Files | | ---- | ----------------------- | ------------------------------------------------ | | 1 | Establish baseline | patterns-evaluation.md (Pattern 4: named runs) | | 2 | Run current version | patterns-evaluation.md (Pattern 1) | | 3 | Compare metrics | patterns-evaluation.md (Patterns 6-7) | | 4 | Analyze failing traces | patterns-trace-analysis.md (Pattern 7) | | 5 | Debug specific failures | patterns-trace-analysis.md (Patterns 8-9) |

Workflow 5: Custom Scorer Development

For creating project-specific evaluation metrics.

| Step | Action | Reference Files | | ---- | --------------------------- | ----------------------------------------- | | 1 | Understand scorer interface | CRITICAL-interfaces.md (Scorer section) | | 2 | Choose scorer pattern | patterns-scorers.md (Patterns 4-11) | | 3 | For multi-agent scorers | patterns-scorers.md (Patterns 13-16) | | 4 | Test with evaluation | patterns-evaluation.md (Pattern 1) |

Reference Files Quick Lookup

| Reference | Purpose | When to Read | | ---------------------------------- | ------------------------ | ----------------------------------------- | | GOTCHAS.md | Common mistakes | Always read first before writing code | | CRITICAL-interfaces.md | API signatures, schemas | When writing any evaluation code | | patterns-evaluation.md | Running evals, comparing | When executing evaluations | | patterns-scorers.md | Custom scorer creation | When built-in scorers aren't enough | | patterns-datasets.md | Dataset building | When preparing evaluation data | | patterns-trace-analysis.md | Trace debugging | When analyzing agent behavior | | patterns-context-optimization.md | Token/latency fixes | When agent is slow or expensive | | user-journeys.md | High-level workflows | When starting a new evaluation project |

Critical API Facts

• Use: mlflow.genai.evaluate() (NOT mlflow.evaluate())
• Data format: {"inputs": {"query": "..."}} (nested structure required)
• predict_fn: Receives **unpacked kwargs (not a dict)

See GOTCHAS.md for complete list.

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