---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 5e85ada2fce8df4247222e5fe899b1108031f932cb4dcb5e267854c91d5f647e
  pageDirectory: concepts
  sources:
    - evaluate-and-monitor-ai-agents-databricks-on-aws.md
  confidence: 0.8
  provenanceState: merged
  freshnessStatus: unverified
  aliases:
    - genai-application-evaluation-lifecycle
    - GAEL
    - GenAI Application Evaluation
    - GenAI application evaluation
    - App Quality Evaluation
    - Evaluate GenAI applications at development
    - GenAI App Evaluation
    - GenAI application development
    - GenAI application lifecycle
    - GenAI applications|GenAI apps
    - Generative AI Application Evaluation
    - RAG Application Evaluation
  citations:
    - file: evaluate-and-monitor-ai-agents-databricks-on-aws.md
title: GenAI Application Evaluation Lifecycle
description: The iterative workflow of developing, evaluating, and monitoring GenAI applications across multiple phases including development testing and production monitoring
tags:
  - mlflow
  - lifecycle
  - genai
timestamp: "2026-06-19T10:22:42.248Z"
---

# GenAI Application Evaluation Lifecycle

The **GenAI Application Evaluation Lifecycle** describes the iterative process of measuring, improving, and maintaining the quality of AI agents, LLMs, RAG systems, and other generative AI applications using MLflow. Unlike traditional ML model evaluation, GenAI applications involve multiple components, multi-turn conversations, and nuanced quality criteria that require specialised evaluation approaches.^[evaluate-and-monitor-ai-agents-databricks-on-aws.md]

## Overview

MLflow provides comprehensive evaluation and monitoring capabilities that span the entire development lifecycle – from testing through production monitoring. The system is designed to help teams iteratively optimise the quality of their GenAI applications. Evaluation and monitoring build upon [MLflow Tracing](/concepts/mlflow-tracing.md), which records execution traces in real time across development, testing, and production phases. These traces can then be evaluated using built-in or custom [LLM Judges](/concepts/llm-judges.md) and [[scorers]], and the same evaluation logic can be reused in production monitoring, ensuring consistent quality assessment throughout the application lifecycle. Domain experts can also provide feedback through an integrated [Review App](/concepts/mlflow-review-app.md) for human feedback, producing additional evaluation data for further iteration.^[evaluate-and-monitor-ai-agents-databricks-on-aws.md]

## Lifecycle Stages

### 1. Development and Testing

During development and testing, traces generated by the application are evaluated using predefined quality criteria. Developers use the evaluation harness to apply LLM judges and scorers to trace data, assessing outputs for correctness, safety, relevance, or other custom metrics. This stage enables rapid iteration on prompts, model selection, and component configuration.^[evaluate-and-monitor-ai-agents-databricks-on-aws.md]

### 2. Production Monitoring

The same judges and scorers used in development can be deployed for [Production Monitoring](/concepts/production-monitoring.md) of the GenAI application. This ensures that the quality metrics collected during testing remain directly comparable to those observed in production. Production monitoring provides continuous visibility into application behaviour and can trigger alerts or retraining when quality degrades.^[evaluate-and-monitor-ai-agents-databricks-on-aws.md]

### 3. Human Feedback Collection

Domain experts can use the Review App to provide direct feedback on application outputs. This human evaluation is crucial for capturing subjective quality aspects – such as helpfulness, tone, or domain-specific accuracy – that automated judges may not fully capture. The collected feedback becomes evaluation data that can be used to retrain or fine-tune judges and to drive further iterations of the application itself.^[evaluate-and-monitor-ai-agents-databricks-on-aws.md]

## Key Concepts

- **MLflow Tracing** – Real-time trace logging that captures execution paths, inputs, and outputs of GenAI applications across the lifecycle.^[evaluate-and-monitor-ai-agents-databricks-on-aws.md]
- **LLM judges and scorers** – Components that evaluate trace data against quality criteria without requiring ground-truth labels. They can be built-in or custom.^[evaluate-and-monitor-ai-agents-databricks-on-aws.md]
- **Evaluation harness** – The mechanism for applying judges and scorers to trace data during development and testing.^[evaluate-and-monitor-ai-agents-databricks-on-aws.md]
- **Consistency across lifecycle** – Reusing the same evaluation logic from development through production enables meaningful quality tracking over time.^[evaluate-and-monitor-ai-agents-databricks-on-aws.md]
- **Iterative workflow** – The entire lifecycle forms a feedback loop: develop → evaluate → deploy → monitor → collect human feedback → iterate.^[evaluate-and-monitor-ai-agents-databricks-on-aws.md]

## Related Concepts

- Agent Evaluation Migration – Updating from MLflow 2 Agent Evaluation to MLflow 3.
- [LLM-as-a-Judge](/concepts/llm-as-a-judge.md) – A common technique for automated quality assessment.
- [Custom Judges](/concepts/custom-judges.md) – Creating domain-specific evaluation criteria.
- [Scheduled Scoring](/concepts/scheduled-scorers-mlflow-genai.md) – Production monitoring for agent outputs.

## Sources

- evaluate-and-monitor-ai-agents-databricks-on-aws.md

# Citations

1. [evaluate-and-monitor-ai-agents-databricks-on-aws.md](/references/evaluate-and-monitor-ai-agents-databricks-on-aws-edcafd11.md)
