LLM Tracing
ELI5 - The Vibe Check
When a chatbot gives a weird answer, 'it's an LLM, who knows' is not a debugging strategy. LLM tracing records every step an AI request actually took: which prompt was sent, what got retrieved for RAG, which tools got called, how many tokens it cost, and what came back at each stage. It turns a black box into something you can actually inspect after the fact, like a flight recorder for your AI pipeline.
Real Talk
LLM tracing captures a structured, timestamped record of every step in an LLM application's execution, including prompts, retrieved context, tool calls, intermediate outputs, token counts, and latency per span, typically visualized as a waterfall or tree. Tools like LangSmith, Langfuse, and Helicone build on standard distributed tracing concepts but add LLM-specific metadata (prompt versions, token cost, model used) needed to debug multi-step agent and RAG pipelines.
When You'll Hear This
"Pull up the trace, I want to see exactly what got retrieved before the model hallucinated." / "LLM tracing showed the agent was calling the same tool three times in a loop."
Related Terms
AI Agent
An AI Agent is an AI system that can think, plan, and act on its own to accomplish a goal.
Distributed Tracing
Distributed tracing is request tracing's big brother — it works across multiple services, servers, and even data centers.
Hallucination
When an AI confidently makes something up — like citing a library that doesn't exist or generating code that calls a function that was never written.
Observability
Observability is the ability to understand what's happening inside your system from the outside, using three types of data: metrics (numbers), logs (events...
RAG (Retrieval Augmented Generation)
RAG is how you give an AI access to your private documents without retraining it.