Langfuse 工作坊 - 端到端的 AI 工程循环

Have the workshop app running locally with both OpenAI and Langfuse credentials in place. From here you can skim 01-base-app, then start building in 02-tracing.

You can customize your experience by changing the phone specs in support-data.ts file. Adding your dad's phone information means, you will get replies for the right type of phone.

This is the blank slate for the tracing step — same code as checkpoint/01-base-app, with no Langfuse wiring yet. The Langfuse packages are already in package.json — run npm inst...

You have a working traced app. The system prompt lives as a constant called SYSTEMPROMPT in src/server/support-agent.ts and is used directly as the system message.

You have a traced app with optional Langfuse-managed prompts. Every chat turn lands in Langfuse as a nested trace.

You have a traced, attributed, monitored app. data/seed-dataset.json and scripts/seed-dataset.ts are already in the repo at this checkpoint.

Your dataset is seeded in Langfuse. scripts/run-dataset.ts is already in the repo.

Your app is traced, monitored, has a hosted dataset, and at least one experiment run with both keywordoverlap and correctness scores. Now you make a change to the app and rerun ...

You have walked through every loop step.