Linking Traces

Overview

When you run evaluations through an AI Connection, Confident AI can link each result back to the trace your AI app produced. This gives you full observability—jump straight from an evaluation result to the exact trace that generated it.

There are two flavors of trace linking:

• Linking test cases to traces for single-turn evaluations
• Linking turns to traces for multi-turn evaluations and red-team attacks

Both work by passing an identifier (testCaseId or turnId) from your payload into your tracing setup.

Linking Test Cases to Traces

For single-turn evaluations, you can link each test case to its corresponding trace for full observability. This is done by including testCaseId in your payload (enabled by default) and passing it to your tracing setup.

Include testCaseId in your payload configuration and ensure your AI connection is configured to accept it.

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{
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  "input": golden.input,
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  "testCaseId": testCaseId
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}

Then, pass the testCaseId to your tracing implementation:

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from deepeval.tracing import observe, update_current_trace
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@observe()
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def llm_app(input: str, test_case_id: str):
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    update_current_trace(test_case_id=test_case_id)
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    response = llm.generate(user=input)
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    return response
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@app.post("/generate")
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def generate(request: dict):
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    test_case_id = request.get("testCaseId")
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    response = llm_app(request["input"], test_case_id)
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    return {"output": response}

Linking Turns to Traces

For multi-turn evaluations and multi-turn red-team attacks, Confident AI calls your endpoint once per turn. Each turn has its own turnId that you can pass to your tracing setup. This links each turn’s trace to the specific turn in the conversation, letting you view traces per-turn from the evaluation or assessment results.

Include turnId in your payload configuration:

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{
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  "input": golden.input,
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  "turnId": turnId,
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  "state": state
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}

Then, pass the turnId to your tracing implementation:

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from deepeval.tracing import observe, update_current_trace
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@observe()
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def llm_app(input: str, turn_id: str):
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    update_current_trace(turn_id=turn_id)
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    response = llm.generate(user=input)
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    return response
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@app.post("/generate")
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def generate(request: dict):
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    turn_id = request.get("turnId")
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    response = llm_app(request["input"], turn_id)
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    return {"output": response}

Next Steps

With traces linked to your evaluation results, you can debug failures end-to-end. Explore related observability and connection features next.