Build and evaluate

What it is: The state sent to the model should contain the information necessary for the question. More content does not automatically mean more quality. Irrelevant documents can make the decision harder and increase cost. Start by identifying which fields support the judgment.

Why learn: Build a request and handle errors while keeping credentials on the server.

Key concepts: context, criteria, and evidence applied to lesson 9.1.

What it is: The API receives model, state, and questions. Each question has an application-chosen key, a type, and instructions. Choice adds a map of alternatives; Score provides a list of ordered levels; Noul may include criteria for true and false. The lab exports an example JSON without credentials.

Why learn: Build a request and handle errors while keeping credentials on the server.

Key concepts: context, criteria, and evidence applied to lesson 9.2.

What it is: An integration must anticipate failure before receiving the first response. Invalid credential, rejected contract, request limits, and unavailability are not the same situation. Repeating an authentication error several times normally only wastes time; temporary overload may allow a controlled retry.

Why learn: Build a request and handle errors while keeping credentials on the server.

Key concepts: context, criteria, and evidence applied to lesson 9.3.

What it is: An evaluation starts with defining what is correct. If labels change from person to person, the metric may be measuring process disagreement. Create a labeling guide, review examples, and document cases that don’t fit well.

Why learn: Compare methods without confusing tuned examples with independent evidence.

Key concepts: context, criteria, and evidence applied to lesson 10.1.

What it is: Use the same task and the same data to compare rules, Jev (a generative model with structured output), and a hybrid flow. Record configurations and versions. Differences in input or criteria make the comparison hard to interpret.

Why learn: Compare methods without confusing tuned examples with independent evidence.

Key concepts: context, criteria, and evidence applied to lesson 10.2.

What it is: When you increase a threshold, normally fewer responses pass to automatic suggestion. This may reduce some errors, but it increases review time and can let through the most confident errors. We need to measure two things together: the quality of accepted cases and coverage, the fraction of cases that the policy accepts.

Why learn: Compare methods without confusing tuned examples with independent evidence.

Key concepts: context, criteria, and evidence applied to lesson 10.3.

What it is: The observation mode means the new stage computes a suggestion, but the previous flow remains responsible for the decision. This lets us compare behavior with real operations without automatically applying the pilot’s mistakes.

Why learn: Prepare logs, monitoring, and feedback without losing control of the operation.

Key concepts: context, criteria, and evidence applied to lesson 11.1.

What it is: A useful log links the event to the question’s version, the policy, and the model used. It also records time, tokens, result, and any eventual correction. Without this information, it’s hard to know whether a behavior change came from the model, the context, or a code change.

Why learn: Prepare logs, monitoring, and feedback without losing control of the operation.

Key concepts: context, criteria, and evidence applied to lesson 11.2.

What it is: A model alias can point to a new version without changing your request. This makes updates easier, but it makes it harder to attribute variation when thresholds were adjusted for a previous version. Record the resolved identifier and pin versions in experiments that need to be reproduced.

Why learn: Prepare logs, monitoring, and feedback without losing control of the operation.

Key concepts: context, criteria, and evidence applied to lesson 11.3.

What it is: Your final project is a triage of customer support tickets in English. Start by describing who receives the tickets, which queues exist, and what problem you want to reduce. Avoid promising full automation. The first deliverable is a specification that someone else can review.

Why learn: Deliver a well-founded adoption decision, even when the answer is not to automate.

Key concepts: context, criteria, and evidence applied to lesson 12.1.

What it is: Present your evaluation so that another person can repeat it. Identify the dataset, its source, the partitions, and the configurations. Show the number of cases per class, the important errors, and the limitations. An accuracy chart without context is not enough.

Why learn: Deliver a well-founded adoption decision, even when the answer is not to automate.

Key concepts: context, criteria, and evidence applied to lesson 12.2.

What it is: The last step is a project decision. There are three legitimate outcomes: continue with a limited deployment, collect more data, or not adopt. The choice depends on quality, cost, coverage, review effort, and operational capacity—not on the popularity of the model.

Why learn: Deliver a well-founded adoption decision, even when the answer is not to automate.

Key concepts: context, criteria, and evidence applied to lesson 12.3.