Legacy documentation — AI Fusion 1.x / 2.x. This article describes AI Fusion versions 1.x and 2.x. For AI Fusion 3, see the current GenAI Data Fusion documentation.
AI Fusion Evaluation Lifecycle
Overview
Evaluating AI agents is not a one-time activity but a continuous lifecycle.
As prompts, models, data, and agentic flows evolve, organizations must repeatedly validate that AI responses remain accurate, relevant, and aligned with business expectations.
The AI Fusion Evaluation Framework supports the full lifecycle of designing, executing, and improving AI evaluations.
This lifecycle connects design-time testing, automated regression validation, and production feedback analysis.
The Evaluation Lifecycle
The lifecycle typically follows five stages:
- Test Design
- Test Data Preparation
- Evaluation Execution
- Results Analysis
- Continuous Improvement
Each stage helps teams systematically improve AI quality over time.
Note: Screenshot needed: Open
/static/aifusion/evaluation. Capture the evaluation workspace showing the suite tree with at least one suite and test visible. Save asimages/evaluation_lifecycle_overview.png
1. Test Design
The first step is defining test scenarios that represent realistic user interactions.
Test cases typically include:
- User prompts or conversation turns
- Expected responses (ground truth)
- Evaluation dimensions and thresholds
Tests represent complete multi-turn conversations customer journeys
These test cases are organized into test suites, allowing teams to validate entire features or business processes.
2. Test Data Preparation
Many AI agents rely on enterprise data. Reliable evaluation therefore requires consistent test data.
The framework supports Data snapshots of the associated data product (micro DB)
This guarantees that tests can be rerun over time with the same data conditions.
3. Evaluation Execution
Once tests are defined, they can be executed against the AI agent.
During execution, the system simulates a real user interaction by sending the conversation prompts to the agent and collecting its responses.
Execution can occur in several modes:
- Interactive testing of individual test case.
- Batch execution of test suites, as regression testing within CI/CD pipeline.
4. Response Evaluation
Responses are evaluated using an LLM-as-a-Judge approach.
An LLM, used during the evaluation process, analyzes each response and scores it across multiple quality dimensions such as:
- Accuracy
- Relevance
- Clarity
- Politeness
The evaluator produces both:
- numeric scores
- explanations describing the reasoning behind the score
This enables evaluation based on semantic correctness rather than exact wording.
5. Results Analysis
Evaluation results provide insights into AI behavior and quality.
Teams can analyze results at multiple levels:
- test suite results
- individual test cases
- conversation turns/steps
- evaluation dimensions
Failures can be investigated to identify issues in prompts, tools, or agent flows.
6. Continuous Improvement
Evaluation results drive improvements in AI systems.
Teams may improve quality by:
- refining prompts
- adjusting agent workflows
- improving data sources
- changing model configurations
Updated systems can then be re-tested using the same evaluation suites, enabling reliable regression testing.
Production Feedback Loop
Real conversations captured from QA or production environments can be imported into the evaluation framework.
Imported conversations can be:
- analyzed for quality issues
- evaluated using the same scoring criteria
- converted into new test cases
This creates a continuous feedback loop between production behavior and design-time testing.
Legacy documentation — AI Fusion 1.x / 2.x. This article describes AI Fusion versions 1.x and 2.x. For AI Fusion 3, see the current GenAI Data Fusion documentation.
AI Fusion Evaluation Lifecycle
Overview
Evaluating AI agents is not a one-time activity but a continuous lifecycle.
As prompts, models, data, and agentic flows evolve, organizations must repeatedly validate that AI responses remain accurate, relevant, and aligned with business expectations.
The AI Fusion Evaluation Framework supports the full lifecycle of designing, executing, and improving AI evaluations.
This lifecycle connects design-time testing, automated regression validation, and production feedback analysis.
The Evaluation Lifecycle
The lifecycle typically follows five stages:
- Test Design
- Test Data Preparation
- Evaluation Execution
- Results Analysis
- Continuous Improvement
Each stage helps teams systematically improve AI quality over time.
Note: Screenshot needed: Open
/static/aifusion/evaluation. Capture the evaluation workspace showing the suite tree with at least one suite and test visible. Save asimages/evaluation_lifecycle_overview.png
1. Test Design
The first step is defining test scenarios that represent realistic user interactions.
Test cases typically include:
- User prompts or conversation turns
- Expected responses (ground truth)
- Evaluation dimensions and thresholds
Tests represent complete multi-turn conversations customer journeys
These test cases are organized into test suites, allowing teams to validate entire features or business processes.
2. Test Data Preparation
Many AI agents rely on enterprise data. Reliable evaluation therefore requires consistent test data.
The framework supports Data snapshots of the associated data product (micro DB)
This guarantees that tests can be rerun over time with the same data conditions.
3. Evaluation Execution
Once tests are defined, they can be executed against the AI agent.
During execution, the system simulates a real user interaction by sending the conversation prompts to the agent and collecting its responses.
Execution can occur in several modes:
- Interactive testing of individual test case.
- Batch execution of test suites, as regression testing within CI/CD pipeline.
4. Response Evaluation
Responses are evaluated using an LLM-as-a-Judge approach.
An LLM, used during the evaluation process, analyzes each response and scores it across multiple quality dimensions such as:
- Accuracy
- Relevance
- Clarity
- Politeness
The evaluator produces both:
- numeric scores
- explanations describing the reasoning behind the score
This enables evaluation based on semantic correctness rather than exact wording.
5. Results Analysis
Evaluation results provide insights into AI behavior and quality.
Teams can analyze results at multiple levels:
- test suite results
- individual test cases
- conversation turns/steps
- evaluation dimensions
Failures can be investigated to identify issues in prompts, tools, or agent flows.
6. Continuous Improvement
Evaluation results drive improvements in AI systems.
Teams may improve quality by:
- refining prompts
- adjusting agent workflows
- improving data sources
- changing model configurations
Updated systems can then be re-tested using the same evaluation suites, enabling reliable regression testing.
Production Feedback Loop
Real conversations captured from QA or production environments can be imported into the evaluation framework.
Imported conversations can be:
- analyzed for quality issues
- evaluated using the same scoring criteria
- converted into new test cases
This creates a continuous feedback loop between production behavior and design-time testing.
