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Overview

This example demonstrates how to test AI chat responses using LLMJudge for semantic validation. Instead of exact text matching, we validate that responses meet behavioral expectations.

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Complete Test

test.json
{
  "name": "AI Chat Quality Tests",
  "version": "1.0.0",
  "tests": [
    {
      "id": "meeting-scheduler",
      "name": "Test Meeting Scheduler Response",
      "pipeline": [
        {
          "id": "mock-ai-response",
          "block": "MockData",
          "config": {
            "data": {
              "text": "I've successfully scheduled your team meeting for tomorrow at 2 PM in Conference Room A. The meeting has been added to your calendar and invitations have been sent to john@example.com.",
              "toolCalls": [
                {
                  "name": "check_user_conflicts",
                  "args": {
                    "date": "2024-01-20",
                    "startTime": "14:00",
                    "endTime": "15:00"
                  }
                },
                {
                  "name": "create_event",
                  "args": {
                    "summary": "Team Meeting",
                    "start": "2024-01-20T14:00:00",
                    "end": "2024-01-20T15:00:00",
                    "location": "Conference Room A",
                    "attendees": ["john@example.com"]
                  }
                }
              ]
            }
          },
          "output": "aiResponse"
        },
        {
          "id": "judge-response",
          "block": "LLMJudge",
          "input": {
            "text": "${aiResponse.text}",
            "toolCalls": "${aiResponse.toolCalls}",
            "expected": {
              "expectedBehavior": "The AI should check for conflicts using check_user_conflicts, then create a meeting using create_event. The response should confirm the meeting is scheduled for tomorrow at 2 PM in Conference Room A and mention that invitations were sent."
            }
          },
          "config": {
            "model": "gpt-4o-mini",
            "criteria": {
              "accuracy": 0.4,
              "completeness": 0.3,
              "relevance": 0.3
            }
          },
          "output": "judgement"
        }
      ],
      "assertions": {
        "judgement.score": { "gt": 0.7 }
      }
    },
    {
      "id": "bad-response-detection",
      "name": "Detect Poor AI Response",
      "pipeline": [
        {
          "id": "mock-bad-response",
          "block": "MockData",
          "config": {
            "data": {
              "text": "I cannot help with that request.",
              "toolCalls": []
            }
          },
          "output": "badResponse"
        },
        {
          "id": "judge-bad",
          "block": "LLMJudge",
          "input": {
            "text": "${badResponse.text}",
            "toolCalls": "${badResponse.toolCalls}",
            "expected": {
              "expectedBehavior": "The AI should check for conflicts and create a meeting with confirmation. It should mention scheduling details and confirm the action was successful."
            }
          },
          "config": {
            "model": "gpt-4o-mini"
          },
          "output": "badJudgement"
        }
      ],
      "assertions": {
        "badJudgement.score": { "lt": 0.4 }
      }
    }
  ]
}

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Setup

You’ll need an OpenAI API key:
.env
OPENAI_API_KEY=sk-proj-your-key-here

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Running the Test

npx semtest test.json
Expected output: 
✅ AI Chat Quality Tests
  ✅ meeting-scheduler: Test Meeting Scheduler Response (1.2s)
     ✅ judgement.score = 0.92 (> 0.7)
     💡 AI used correct tools and provided complete confirmation

  ✅ bad-response-detection: Detect Poor AI Response (0.8s)
     ✅ badJudgement.score = 0.15 (< 0.4)
     💡 Response failed to perform required actions

2 tests passed, 0 failed (2.0s total)

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Breaking It Down

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Good Response Test

1

Mock AI Response

Simulate a successful AI response with tool calls:
{
  "block": "MockData",
  "config": {
    "data": {
      "text": "I've successfully scheduled...",
      "toolCalls": [...]
    }
  }
}
2

Semantic Validation

Use LLMJudge to validate the response semantically:
{
  "block": "LLMJudge",
  "input": {
    "text": "${aiResponse.text}",
    "toolCalls": "${aiResponse.toolCalls}",
    "expected": {
      "expectedBehavior": "Should check conflicts and create meeting..."
    }
  }
}
3

Assert High Score

Verify the AI response meets quality threshold:
{
  "assertions": {
    "judgement.score": { "gt": 0.7 }
  }
}

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Bad Response Detection

The second test verifies that poor responses get low scores, ensuring your LLMJudge criteria are working correctly: 
{
  "text": "I cannot help with that request.",
  "toolCalls": []
}
This incomplete response should score below 0.4 because it:
  • Doesn’t use required tools
  • Doesn’t provide requested information
  • Fails to complete the task

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Custom Criteria

Adjust scoring weights based on what matters most: 
{
  "config": {
    "criteria": {
      "accuracy": 0.5,      // Most important: is it correct?
      "completeness": 0.3,  // Did it cover everything?
      "relevance": 0.2      // Is it on-topic?
    }
  }
}

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Real-World Usage

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Testing OpenAI Chat API

{
  "pipeline": [
    {
      "block": "HttpRequest",
      "input": {
        "url": "https://api.openai.com/v1/chat/completions",
        "method": "POST",
        "headers": {
          "Authorization": "Bearer ${env.OPENAI_API_KEY}",
          "Content-Type": "application/json"
        },
        "body": {
          "model": "gpt-4o-mini",
          "messages": [
            {
              "role": "user",
              "content": "Schedule a team meeting for tomorrow at 2pm"
            }
          ],
          "tools": [...]
        }
      },
      "output": "response"
    },
    {
      "block": "JsonParser",
      "input": "${response.body}",
      "output": { "parsed": "aiData" }
    },
    {
      "block": "LLMJudge",
      "input": {
        "text": "${aiData.choices[0].message.content}",
        "toolCalls": "${aiData.choices[0].message.tool_calls}",
        "expected": {
          "expectedBehavior": "Should confirm meeting is scheduled..."
        }
      },
      "output": "validation"
    }
  ]
}

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Cost Considerations

LLMJudge uses GPT-4o-mini by default, which costs approximately:
  • ~$0.0001 per evaluation
  • ~$0.001 for 10 tests
  • ~$0.10 for 1000 tests
For production CI/CD, consider:
  1. Testing a sample of responses (not every one)
  2. Using faster traditional validation where possible
  3. Running full semantic tests nightly

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Next Steps

Semantic Validation

Deep dive into LLMJudge

Tool Call Validation

Validate AI tool usage

Calendar Agent

Complete calendar agent example

LLMJudge Block

Complete LLMJudge documentation