Author: Katrina Collins
AI evaluation platforms help teams run and analyze evaluations. TestRail connects those results to persistent test cases, expected behaviors, coverage, governance, and release workflows—without replacing the evaluation tools teams already use.
TL;DR
- AI evaluation platforms and test management platforms support different but complementary parts of the AI quality process.
- Modern AI eval tools help teams manage datasets, score outputs, run experiments, compare prompts and models, review results, track regressions, and trace AI system behavior.
- TestRail extends that work into broader software testing and release workflows. Evaluation scenarios can be managed as persistent test cases with defined steps and expected behaviors, organized into reusable suites, tracked for coverage, and connected to requirements, milestones, versions, and releases.
- The test case is the bridge between a dynamic evaluation and a repeatable QA workflow. It preserves what the team intended to evaluate, what behavior it expected, and how the scenario performed across changes.
- When an evaluation fails or quality declines, TestRail helps teams turn that result into actionable testing work by making failing scenarios visible, supporting organized review, maintaining ownership and context, and tracking regressions through later test runs.
- TestRail’s AI Evaluation Template brings results from evaluation and observability platforms into structured testing workflows. Teams can capture inputs, outputs, traces, test status, evaluation reasoning, and a configurable, multi-category quality rating.
- The Quality Insights Dashboard gives teams visibility into failing scenarios, regression trends, model and version comparisons, release-level quality, and quality drift over time.
- TestRail does not replace AI evaluation platforms. It connects their results to the QA, governance, and release workflows used to manage the rest of the product’s quality.
AI teams have never had more capable ways to evaluate models, agents, and AI-powered applications.
Platforms such as Langfuse, LangSmith, DeepEval, OpenAI Evals, Arize Phoenix, and Promptfoo help teams score outputs, manage datasets, compare prompts and models, run experiments, review results, capture traces, and understand how AI systems perform.
Many modern evaluation platforms also support annotations, regression tracking, review queues, comparisons, and prompt or model version history. These capabilities are essential for evaluating AI behavior and improving models, prompts, and applications.
The next challenge is connecting that work to the broader software testing processes organizations use to manage product quality.
An evaluation platform may help a team understand how a model performed against a dataset or during a particular experiment. Test management connects the relevant scenarios and results to persistent test cases, expected behaviors, requirements, coverage, test suites, milestones, and release workflows.
AI evaluation and test management are not competing approaches. They are complementary parts of a more complete AI quality process.
Where test management fits in the AI quality stack
AI quality work generally spans several connected layers:
- The application layer
- The model or agent layer
- The observability layer
- The evaluation layer
- The quality-management layer
These layers can overlap, and modern platforms may support capabilities across more than one of them.
The application and model layers support the systems teams are building. Observability platforms help teams understand traces, latency, behavior, and production signals. Evaluation platforms help teams manage datasets, run evaluations, compare experiments, review results, and analyze model or application performance.
The quality-management layer connects that work to broader software testing and release processes.
It is where evaluation scenarios can be managed as persistent test cases, expected behaviors can be documented, related scenarios can be organized into reusable suites, and results can be connected to requirements, coverage, milestones, versions, and releases.
The distinction is not that evaluation platforms cannot track quality. Many provide sophisticated tools for evaluating, comparing, and improving AI systems.
The distinction is one of workflow focus:
- Evaluation platforms optimize for running and analyzing AI evaluations.
- TestRail manages AI evaluation scenarios and results within broader software testing, QA governance, and release workflows.
Together, these layers help teams move from understanding evaluation results to managing AI quality alongside the rest of the product.
The test case connects AI evals to broader QA workflows
Modern AI evaluation platforms are designed to evaluate outputs dynamically, manage datasets, benchmark prompts, compare experiments, review results, and trace model behavior.
TestRail adds a complementary structure: persistent test scenarios defined as test cases with steps, expected behaviors, evaluation criteria, and test history.
That structure matters because an evaluation result records what happened during a particular run or experiment. A test case preserves what the team intended to evaluate, what outcome or behavior it expected, and how that scenario should be tested again.
For example, an evaluation platform may score the factual accuracy or relevance of a chatbot response. The corresponding test case can preserve:
- The user scenario being evaluated
- The input or prompt
- Any setup or testing steps
- The expected behavior
- The quality dimensions being assessed
- The result from each model or application version
- Supporting traces and evaluation reasoning
- The related requirement, milestone, or release
When the model, prompt, retrieval system, dataset, or application changes, the team can rerun the same scenario rather than reconstructing it from an earlier experiment.
By connecting AI evaluation results to test cases, teams can:
- Preserve important evaluation scenarios over time
- Document steps, inputs, and expected behaviors
- Organize related scenarios into reusable test suites
- Track coverage across requirements, risks, and behaviors
- Identify scenarios that still need to be evaluated
- Compare results across models, configurations, versions, and releases
- Connect evaluation evidence to milestones and release-readiness reviews
- Maintain traceability from a requirement or expected behavior to its test history
- Apply established QA review, reporting, and approval processes
- Manage AI testing alongside manual and automated software testing
This is the bridge between AI evaluation and broader software quality management.
Evaluation platforms continue to provide datasets, scores, traces, comparisons, and experiment-level insights. TestRail connects that work to a persistent QA system where scenarios, coverage, history, governance, and release evidence can be managed together.
When AI eval failures need to become actionable testing work
As AI adoption grows, teams are running more evaluations across more prompts, models, datasets, configurations, and product versions.
An estimated 53% of organizational code is now AI-generated or AI-assisted, and 61% of teams report moderate-to-dramatic increases in QA testing demand as a result, according to the Sembi Software Quality Pulse Report.
Modern evaluation platforms give teams increasingly sophisticated ways to run evaluations, compare experiments, and identify failing or degraded outputs. The next challenge is turning those findings into actionable testing work within the broader QA process.
When an evaluation fails or a score declines, teams still need to determine:
- Which test scenario and expected behavior were affected
- Whether the issue is isolated or part of a broader regression
- How significant the failure is
- Who owns the investigation or follow-up
- Which scenarios require manual review
- Whether related test cases should be rerun
- How the issue affects quality risk and release-readiness criteria
- Whether the failure has been resolved in a later version
TestRail connects evaluation results to persistent test cases, suites, test runs, milestones, and releases. This gives teams visibility into failing scenarios and a structured place to review results, assign ownership, document context and severity, and track the outcome over time.
Failed or degraded evaluations can become part of an organized testing workflow rather than remaining isolated findings. Teams can group related failures, review their impact, rerun affected suites, and compare results with earlier versions to determine whether a regression has been resolved.
Automated evaluation results and manual reviews can also sit within the same testing structure. A result generated by an LLM-as-a-judge can be reviewed alongside human feedback, standard test statuses, quality ratings, and supporting traces.
This makes it easier to move from identifying an AI quality issue to managing it through an established QA process.
Evaluation platforms remain essential for detecting and analyzing changes in AI performance. TestRail helps teams operationalize those findings through organized review workflows, regression management, ownership, traceability, and release-level visibility.
From dynamic evaluations to reusable test coverage
AI evaluation work is often dynamic.
Teams may adjust prompts, switch models, update datasets, change retrieval sources, revise evaluation criteria, or compare multiple experiments. Evaluation platforms are designed to support that iterative process.
At the same time, QA teams need a stable set of scenarios they can return to as the system evolves.
Persistent test cases provide that baseline.
A test case can represent a critical customer interaction, an accuracy requirement, an expected refusal behavior, a high-risk edge case, a policy requirement, or another scenario that should continue to be evaluated across versions.
Related test cases can be organized into reusable suites, such as:
- Core functionality
- Accuracy and relevance
- Safety and policy behavior
- Retrieval-augmented generation
- Adversarial and edge-case testing
- Performance and latency
- Release regression testing
Teams can rerun these suites after changes and compare the results with earlier versions. They can also connect the test cases to requirements, milestones, and releases.
This creates reusable coverage within the broader QA process while preserving the flexibility of the underlying evaluation tools.
The goal is not to make probabilistic AI behavior appear deterministic. It is to define important scenarios, document expected behaviors, evaluate them consistently, examine variation, and understand quality risk before deployment.
How TestRail connects AI evals to software testing
TestRail provides a structured destination for the evaluation scenarios and results that need to become part of the organization’s broader software testing process.
TestRail’s AI Evaluation Template is designed to bring AI evaluation results into structured testing and release workflows. Teams can continue running evaluations through Langfuse, LangSmith, DeepEval, OpenAI Evals, Arize Phoenix, Promptfoo, Braintrust, or an internal evaluation pipeline.
Those platforms continue to perform the work they are designed for, which may include:
- Managing datasets
- Running evaluations and experiments
- Comparing prompts, models, and versions
- Generating scores and traces
- Supporting annotations and reviews
- Identifying regressions
- Producing model-level insights
TestRail connects relevant scenarios and results to broader QA workflows.
Evaluation scenarios can be represented as persistent test cases with defined steps and expected behaviors. Related cases can be organized into reusable suites, associated with requirements and risks, tracked for coverage, and connected to milestones or releases.
This allows teams to review AI evaluation results alongside the rest of a release’s testing evidence, including manual tests, automated tests, API changes, user-interface updates, integrations, and security requirements.
Because AI output can be non-deterministic, the same input may produce different outputs across runs. A binary pass-or-fail status may still be useful, but it does not always communicate the full quality of an AI response.
The AI Evaluation Template addresses this by placing a configurable, multi-category quality rating alongside the usual test status. Teams can assess dimensions such as accuracy, safety, consistency, groundedness, and relevance while retaining the established structure of test cases, test runs, suites, milestones, and reports.
Each evaluation result can capture:
- The input provided to the AI system
- The output generated
- Supporting traces
- Response time or latency
- The standard test status
- Configurable quality ratings
- Evaluation reasoning or comments
- Human or automated evaluation results
- The model, version, configuration, milestone, or release being tested
The Quality Insights Dashboard helps teams move beyond a generic view of evaluation scores. It can provide visibility into:
- Failing scenarios that require attention
- Regression trends across repeated evaluations
- Differences between models, versions, or configurations
- Release-level views of AI quality
- Quality drift over time
These views help teams identify where results are degrading, determine which scenarios require review, compare the impact of system changes, and assess quality risk and release readiness with clearer evidence.
Human reviews and LLM-as-a-judge results can sit within the same testing structure, helping teams compare evaluation methods while maintaining traceability to the original scenario and expected behavior.
TestRail does not determine that an AI system is safe to deploy. It gives teams organized testing evidence they can use to identify coverage gaps, examine trends, triage failures, manage regressions, support governance processes, understand quality risk, and assess release readiness.
How AI evaluation data maps into TestRail
The AI Evaluation Template gives teams a consistent structure for bringing evaluation data into TestRail:
| TestRail field | Evaluation data |
| Input | User prompt |
| Output | Model response |
| Traces | Trace URL |
| Latency | Response time |
| Status | Standard test result status |
| Quality rating | Fully customizable quality categories |
| Comment | Evaluation reasoning |
To learn how to connect an evaluation or observability platform to TestRail through the API, follow the integration guide for step-by-step instructions.
The takeaway
AI evaluation platforms and test management platforms solve different parts of the same quality process.
Evaluation platforms help teams manage datasets, run and compare evaluations, analyze outputs, trace behavior, review results, and improve models, prompts, and AI applications.
TestRail connects relevant scenarios and results to persistent test cases with steps and expected behaviors. It helps teams organize reusable test suites, manage coverage, maintain traceability, triage failures, combine manual and automated reviews, manage regressions, and connect evaluation evidence to milestones and releases.
The Quality Insights Dashboard gives teams visibility into failing scenarios, regression trends, differences across models or versions, release-level quality, and quality drift over time. This helps teams identify where results are degrading, determine which scenarios require attention, and assess quality risk and release readiness with clearer evidence.
That test-case structure is the bridge between dynamic AI evaluation work and broader software testing.
TestRail does not replace the evaluation tools already in your stack. It extends their results into the QA, governance, and release processes used to manage the rest of your product’s quality.
See how the AI Evaluation Template brings AI evaluation results into TestRail—start a free trial, or read the integration guide to connect the tools you already use.
Frequently asked questions
What’s the difference between AI evaluation and AI quality management?
AI evaluation platforms help teams run and analyze evaluations using datasets, experiments, scoring, comparisons, annotations, traces, and regression tracking. They help teams understand how a model, agent, or AI application performs across prompts, scenarios, and versions.
AI quality management connects relevant evaluation scenarios and results to broader software testing processes. It organizes them into persistent test cases and reusable suites, tracks coverage and test history, supports review and regression workflows, and connects testing evidence to requirements, milestones, and releases.
The two functions are complementary. AI evaluation helps teams analyze and improve AI behavior, while AI quality management helps them manage that work within established QA and release workflows.
What is the AI quality stack?
The AI quality stack describes the layers involved in building, observing, evaluating, and managing AI systems. It typically includes the application layer, model or agent layer, observability layer, evaluation layer, and quality-management layer.
Many teams already use observability and evaluation tools to trace AI behavior, manage datasets, benchmark prompts, compare experiments, and score outputs. The quality-management layer connects relevant evaluation scenarios and results to a structured testing process, where they can be organized into persistent test cases and suites, tracked for coverage, monitored for regressions, and connected to requirements, milestones, releases, and broader QA workflows.
Where test management fits in the AI quality stack
AI evaluation tools are excellent at scoring outputs, benchmarking prompts, comparing experiments, managing datasets, and tracing model behavior. Test management complements those capabilities by connecting relevant evaluation scenarios and results to the broader software testing and release process.
TestRail complements these tools by connecting relevant evaluation scenarios and results to persistent test cases, expected behaviors, reusable test suites, requirements, coverage, milestones, and releases. This helps teams turn evaluation findings into actionable testing work, manage regressions, combine automated and manual reviews, and assess quality risk and release readiness within established QA workflows.
Can I connect TestRail to tools like Langfuse, DeepEval, or OpenAI Evals?
Yes. Using TestRail’s AI Evaluation Template and APIs, teams can send evaluation results from virtually any AI evaluation or observability platform into TestRail as structured test results.
This includes platforms such as Langfuse, LangSmith, DeepEval, OpenAI Evals, Arize Phoenix, Promptfoo, Braintrust, and custom evaluation pipelines. Teams can connect prompts, outputs, traces, scores, quality ratings, model metadata, and evaluation reasoning to persistent test cases and broader quality workflows in TestRail.
What is the TestRail AI Evaluation Template?
The AI Evaluation Template is a purpose-built TestRail template for evaluating non-deterministic AI systems, where the same input may produce different outputs and a binary pass/fail result alone is often insufficient.
The template captures inputs, outputs, traces, model metadata, and evaluation reasoning. It also supports configurable, multi-category quality ratings for dimensions such as accuracy, safety, consistency, groundedness, relevance, and custom criteria defined by the team.
This allows teams to combine quantitative signals, such as scores and latency, with qualitative assessments from human reviewers or LLM-as-a-judge evaluations. Results can then be managed through TestRail test cases, suites, dashboards, reports, and release workflows to help teams compare models or versions, monitor failing scenarios and regression trends, assess release-level quality, and understand quality drift over time.
Does managing AI quality in TestRail replace my evaluation tools?
No. TestRail complements AI observability and evaluation platforms rather than replacing them.
Evaluation tools continue to manage datasets, run experiments, generate traces, benchmark outputs, compare models or prompts, and score AI behavior. TestRail connects relevant evaluation scenarios and results to persistent test cases, expected behaviors, coverage, review workflows, milestones, releases, and broader QA processes.
The goal is not to replace the AI tools already in your stack. It is to give relevant evaluation results a shared destination where teams can organize them, track them over time, turn failures into actionable testing work, and manage them alongside the rest of their software testing activities.
