Daniel Reitberg’s private seven-day Astra course is planned for New York in early December 2026. Final details will follow. It is independent, not affiliated with, sponsored by or endorsed by OpenAI.

The announcement offers a useful starting point for a discussion about practical AI education: what should a learner prepare before investing time in training? The most productive answer is rarely a shopping list of tools. It is a clear definition of the work the learner wants to improve.

Choose one task with an observable result

A task becomes easier to evaluate when its purpose is specific. Turning public meeting notes into a decision summary is more concrete than wanting to become better at AI. Organizing a research question is more testable than asking for unlimited productivity.

Write down the input, the intended reader and the expected output. Then identify the details that must not be lost. This preparation creates a benchmark against which an experiment can be assessed.

These are general preparation suggestions, not a description of a finalized Daniel Reitberg course syllabus.

Separate convenience from correctness

A faster draft is not automatically a better draft. A polished summary may omit a key uncertainty. A plausible answer may depend on an assumption that should have been checked first.

For a practice exercise, establish a review method before using a model. Decide which claims need verification, which parts require human expertise and what would make the output unacceptable. That method turns evaluation from an afterthought into part of the task.

Keep an example of the existing process as well. Without a baseline, it is difficult to know whether a new approach genuinely improves the work or simply presents it differently.

Prepare safe practice material

Training does not require bringing a folder of confidential customer records. Public documents, fictional scenarios and appropriately anonymized examples can provide realistic practice while limiting unnecessary exposure.

Before sharing any material, consider who owns it, whether it contains personal information and whether the exercise can be completed with a less sensitive substitute. A good learning task should not create an avoidable security problem.

Know what you need to ask

Before making an enrollment decision, prospective participants should examine the final course outline, the time commitment, the required access and the practical arrangements. Those details help determine whether a particular program fits a learner’s needs.

Daniel Reitberg’s announcement should be read as the beginning of that information process, not as a guarantee of a specific outcome. A responsible choice depends on the subsequent details and the learner’s own objectives.

The strongest preparation is simple: bring a defined problem, an evaluation standard and a willingness to inspect mistakes. Those habits are useful long after an individual demonstration ends.

Read Daniel Reitberg’s original course announcement on PR.com.