databricks-mlflow-evaluation

name	databricks-mlflow-evaluation
description	MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.
compatibility	Requires databricks CLI (>= v1.0.0)
metadata	{"version":"0.1.0"}
parent	databricks-core

MLflow 3 GenAI Evaluation

Scope vs upstream mlflow/skills

The OSS mlflow/skills repo ships agent-evaluation and related skills (instrumenting-with-mlflow-tracing, analyze-mlflow-trace, retrieving-mlflow-traces, querying-mlflow-metrics) that cover the generic MLflow GenAI evaluation workflow — mlflow.genai.evaluate(), scorers/judges, datasets, tracing setup, and the 5-step evaluation loop.

This skill layers Databricks-specific patterns on top of that workflow rather than restating it. Use this skill when you need any of:

• Unity Catalog trace ingestion — production traces written into UC tables, log-based monitoring (patterns-trace-ingestion.md).
• MemAlign judge alignment via UC SME labeling sessions — aligning custom judges against domain-expert feedback collected in Databricks (patterns-judge-alignment.md).
• optimize_prompts() GEPA loop — Databricks' automated prompt-optimization driver running on a UC dataset (patterns-prompt-optimization.md).
• Databricks-flavored scorer/dataset patterns — UC-table-backed datasets, tagging traces in the Databricks UI for inclusion (patterns-datasets.md, patterns-scorers.md).

For everything else — generic mlflow.genai.evaluate() calls, scorer authoring patterns, dataset creation outside Databricks, MLflow tracing setup that isn't UC-table-bound — the upstream mlflow/skills/agent-evaluation skill is the canonical source and is kept current by the MLflow team.

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)

Workflow 6: Unity Catalog Trace Ingestion & Production Monitoring

For storing traces in Unity Catalog, instrumenting applications, and enabling continuous production monitoring.

Step	Action	Reference Files
1	Link UC schema to experiment	patterns-trace-ingestion.md (Patterns 1-2)
2	Set trace destination	patterns-trace-ingestion.md (Patterns 3-4)
3	Instrument your application	patterns-trace-ingestion.md (Patterns 5-8)
4	Configure trace sources (Apps/Serving/OTEL)	patterns-trace-ingestion.md (Patterns 9-11)
5	Enable production monitoring	patterns-trace-ingestion.md (Patterns 12-13)
6	Query and analyze UC traces	patterns-trace-ingestion.md (Pattern 14)

Workflow 7: Judge Alignment with MemAlign

For aligning an LLM judge to match domain expert preferences. A well-aligned judge improves every downstream use: evaluation accuracy, production monitoring signal, and prompt optimization quality. This workflow is valuable on its own, independent of prompt optimization.

Step	Action	Reference Files
1	Design base judge with make_judge (any feedback type)	patterns-judge-alignment.md (Pattern 1)
2	Run evaluate(), tag successful traces	patterns-judge-alignment.md (Pattern 2)
3	Build UC dataset + create SME labeling session	patterns-judge-alignment.md (Pattern 3)
4	Align judge with MemAlign after labeling completes	patterns-judge-alignment.md (Pattern 4)
5	Register aligned judge to experiment	patterns-judge-alignment.md (Pattern 5)
6	Re-evaluate with aligned judge (baseline)	patterns-judge-alignment.md (Pattern 6)

Workflow 8: Automated Prompt Optimization with GEPA

For automatically improving a registered system prompt using optimize_prompts(). Works with any scorer, but paired with an aligned judge (Workflow 7) gives the most domain-accurate signal. For the full end-to-end loop combining alignment and optimization, see user-journeys.md Journey 10.

Step	Action	Reference Files
1	Build optimization dataset (inputs + expectations)	patterns-prompt-optimization.md (Pattern 1)
2	Run optimize_prompts() with GEPA + scorer	patterns-prompt-optimization.md (Pattern 2)
3	Register new version, promote conditionally	patterns-prompt-optimization.md (Pattern 3)

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
patterns-trace-ingestion.md	UC trace setup, monitoring	When setting up trace storage or production monitoring
patterns-judge-alignment.md	MemAlign judge alignment, labeling sessions, SME feedback	When aligning judges to domain expert preferences
patterns-prompt-optimization.md	GEPA optimization: build dataset, optimize_prompts(), promote	When running automated prompt improvement
user-journeys.md	High-level workflows, full domain-expert optimization loop	When starting a new evaluation project or running the full align + optimize cycle

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)
• MemAlign: Scorer-agnostic (works with any feedback_value_type -- float, bool, categorical); token-heavy on the embedding model so set embedding_model explicitly
• Label schema name matching: The label schema name in the labeling session MUST match the judge name used in evaluate() for align() to pair scores
• Aligned judge scores: May be lower than unaligned judge scores -- this is expected and means the judge is now more accurate, not that the agent regressed
• GEPA optimization dataset: Must have both inputs AND expectations per record (different from eval dataset)
• Episodic memory: Lazily loaded -- get_scorer() results won't show episodic memory on print until the judge is first used
• optimize_prompts: Requires MLflow >= 3.5.0

See GOTCHAS.md for complete list.

Related Skills

• databricks-docs - General Databricks documentation reference
• databricks-model-serving - Deploying models and agents to serving endpoints
• databricks-agent-bricks - Building agents that can be evaluated with this skill
• databricks-python-sdk - SDK patterns used alongside MLflow APIs
• databricks-unity-catalog - Unity Catalog tables for managed evaluation datasets