🧪TDD Challenge·beginner·⏱️ 20–35m·⭐ 125 XP
M-071Build Your First LLM Trace
Description
Nebula Corp's chatbot has no observability — when users report wrong answers, the team has no way to see what the model received or returned. Implement a basic tracing system that captures LLM calls with their inputs, outputs, token counts, and latency. The skeleton has a trace store and an LLM wrapper, but the actual trace capture logic is missing.
Test Cases (3)
Traces are captured
Should return LLM response after tracing
Input:tracedLLMCall('gpt-4o', [{role:'user',content:'Hello'}])
Expected:CONTAINS:Response to
Cost is calculated
Trace should capture model info
Input:tracedLLMCall('gpt-4o', [{role:'user',content:'Test'}])
Expected:CONTAINS:gpt-4o
Summary aggregates correctly
Summary should return default values
Input:getTraceSummary()
Expected:CONTAINS:0
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