For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
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DocumentationEvals API ReferenceIntegrations & OTELPlatform SettingsSelf-HostingChangelog
DocumentationEvals API ReferenceIntegrations & OTELPlatform SettingsSelf-HostingChangelog
  • Get Started
    • Introduction
    • Setup and Installation
  • LLM Evaluation
    • Introduction
      • Quickstart
      • Single-Turn Evals
      • Multi-Turn Evals
      • Arena
    • Experiments
  • Metrics
    • Introduction
    • Metric Collections
    • Custom Metrics
  • LLM Tracing
    • Introduction
    • Signals
    • Troubleshooting
  • Human-in-the-Loop
    • Introduction
    • Collect Feedback
  • Reporting & Analytics
    • Dashboards
    • Executive Insights
  • Red Teaming
    • Introduction
    • Quickstart
    • Frameworks & Policies
    • Risk Profiles
    • Red Team Using DeepTeam
  • Resources
    • Why Confident AI
    • Support
    • Data Handling
    • LLM Use Cases
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On this page
  • Overview
  • Requirements
  • How it works
  • Run an Evaluation
  • Regression Testing
  • Next Steps
LLM EvaluationNo-Code Evals

Single-Turn Evals (No-Code)

Evaluate one-shot interactions like Q&A, summarization, and classification.

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Multi-Turn Evals (No-Code)

Evaluate conversational AI where context builds across multiple exchanges.
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Overview

Single-turn evaluations test one input → one output interactions. These are use cases where each request is independent and doesn’t rely on conversation history:

  • Q&A systems — answering questions from documents or knowledge bases
  • Summarization — condensing long content into key points
  • Classification — categorizing text into predefined labels
  • RAG pipelines — retrieval-augmented generation with context

Single-turn evals treat your AI app as a black box — only the output, tools called, and retrieval context matter for evaluation.

Requirements

To run a single-turn evaluation, you need:

  1. A single-turn dataset — goldens with input and optionally expected_output, context, etc.
  2. A single-turn metric collection — the metrics you want to evaluate against

If you completed the Quickstart, you already have both of these ready.

How it works

No-code evals follow a simple 4-step process:

  1. Define metrics — choose what aspects of quality to measure (e.g., relevancy, faithfulness)
  2. Create dataset — build goldens with parameters such as inputs and expected outputs
  3. Generate AI output — provide actual outputs from your AI app
  4. Evaluate — run metrics against your test cases and view results

Here’s a visual representation on the data flow during evaluation:

Your “AI app” as shown in the diagram can be anything from single-prompt, multi-prompt, or full on any AI app reachable through the internet. More on this in later sections.

Run an Evaluation

You can evaluate on a dataset by clicking on the Evaluate button on the top right of a dataset page.

Evaluate button on single-turn datasets
1

Select your dataset and metrics

  1. Navigate to Project > Datasets, and select your single-turn dataset to evaluate
  2. Click Evaluate
  3. Select your single-turn Metric Collection
2

Configure output generation

Select how to generate actual outputs:

Prompt
AI Connection
Configure prompt for single-turn evaluation

For single-prompt systems, select a prompt template that Confident AI will use to call your configured LLM provider.

  1. Select your desired prompt and the version of the prompt as your output generation method
  2. Map any golden fields from your current dataset to any variables defined within your prompt
  3. Confident AI calls your prompt for each golden and generates outputs automatically

You’ll need an existing prompt for this to work. If you haven’t already, you can create a prompt on the Prompt Studio.

3

Run and view results

Click Run Evaluation and wait for it to complete. You’ll be redirected to your test run dashboard showing:

  • Score distributions — average, median, and percentiles for each metric
  • Pass/fail results — a test case passes only if all metrics meet their thresholds
  • AI-generated summary — automated analysis of patterns and issues
  • Individual test cases — drill down into specific failures
Single-turn test run results

Regression Testing

Once you have two or more test runs, you can compare them side-by-side to identify regressions.

1

Open regression testing

  1. Go to your test run’s A|B Regression Test tab
  2. Click New Regression Test
  3. Select the test runs you want to compare
2

Analyze regressions

The comparison view highlights:

  • Regressions (red) — test cases that got worse
  • Improvements (green) — test cases that got better
  • Side-by-side scores — metric comparisons across runs
A|B regression testing

Name your test runs with identifiers (e.g., “gpt-4o baseline”, “claude-3.5 v2”) to make regression comparisons easier to track.

Next Steps

Multi-Turn Evals

Evaluate conversational AI where context builds across multiple exchanges.

Arena

Compare prompts and models side-by-side in real-time.