ติดตามและตรวจสอบการเรียกใช้ LLM

An LLM app feels like a black box until you log what it does. Tracing records each call so you can debug, cost, and improve it later. 🔍

For every call, log the final prompt, the model name, and key parameters. This request record lets you reproduce any answer exactly.

Save the generated text alongside the request. Together the input and output form one trace you can replay, score, or share with a teammate.

Each response reports tokens in and out. Logging tokens per call is how you attribute cost and spot prompts that quietly grew too large.

Multiply token counts by the price per token to get spend. Tracking cost per request keeps a runaway feature from surprising you on the bill.

Time each call from send to final token. Watching latency per request reveals slow prompts and tells you when streaming would help users.

A real request may chain retrieval, the LLM, and a tool. Each step is a span, and nesting them into one trace shows where time and tokens go.

Platforms like LangSmith, Langfuse, and Phoenix capture traces with one wrapper. A purpose-built observability tool beats parsing raw logs by hand.

Attach a user id, prompt version, and feature name to each trace. This metadata lets you slice metrics and find which segment is misbehaving.

At high volume, logging every full payload is costly. Sampling a fraction of traces keeps insight while controlling storage and privacy.

Roll traces into dashboards for cost, latency, and error rate, then alert on spikes. Now production problems reach you before users complain.

Your monthly LLM bill jumped with no traffic change. Which logged value explains it fastest?

You can now trace LLM calls end to end: request, response, tokens, cost, latency, and spans, then watch them on dashboards. Eyes wide open! 🎉