Our summary
This Chinese course explains how to connect Jev's judgments to software without handing it control of every action. Eleven chapters move from questions and evidence to practical recipes, simulations, evaluation and assistant workflows. Readers need some Python knowledge to follow the notebooks, but the chapter maps explain what each part teaches.
The course follows a chain: supply allowed evidence, define a question, read its probabilities, apply rules and check the resulting action. It offers invented examples, saved responses and optional new questions sent to the AI service. An invented response explains how the program makes a decision; a saved response describes one past run.
Successful examples do not establish accuracy for new tasks. Asking the AI service may cost money, and some notebooks do so when an access key is present. The local model chapter studies Laya, a separate model, not Jev running on your computer. Check each source's dates, sample sizes and permissions before reusing its results or material.
Key takeaways
- Use the chapter map to choose a route: basic questions first, practical recipes next, then evaluation or assistant integration as needed.
- Separate invented examples, saved model responses and new questions sent to the AI service. Not every notebook works the same way without a connection, and saved output is not a fresh test.
- Keep permissions and action checks in code. Compare models on fixed tasks, including failed or missing answers, rather than quoting only successful results.