Practise the thinking. Keep the judgement.
Use Ronri alongside an AI course: notes, experiments and recall
A study workflow for an AI lecture or online course that separates source notes, a real coding experiment, checked cards and a later explanation.
How do I choose what to learn from an AI course?
Start with the course’s prerequisites and one concrete outcome, such as explaining a training and validation split. Keep the course title, lecture and source location in your note. A study assistant can help organise your work, but it cannot establish that a course prepares you for a particular job.
Use Ronri and RubricMark alongside your chosen course. Keep definitions and open questions in a note or outline, then select one thing to explain without looking. Different university materials may use different examples and depth; follow the definitions and requirements of your own course.
Is a study notebook the same as an AI coding notebook?
No. A coding notebook runs code and records computational outputs. Ronri’s study notes and the website’s Outliner organise your understanding. Use a suitable coding environment for the actual experiment, and record its setup, result and interpretation in your study material.
For a small experiment, write “Question — Setup — Observation — Explanation”. Keep the data split and evaluation conditions visible. A neat summary or a generated explanation is not proof that code ran or that a model generalises beyond the tested data.
How do I check an AI-generated explanation?
Compare the explanation with the lecture or a primary source. Verify definitions, quantities and claims about what the experiment shows. If the source does not support a statement, mark it unresolved rather than copying it into a card as a fact.
For example, a low training error alone does not tell you the validation error. Make a note that separates what you observed from what you still need to measure. Stanford CRAFT’s fact-checking resource provides a useful starting point for practising evaluation of AI responses.
How do I turn a lecture into a later review?
After checking the note, make a few cards with clear answers, such as “Which data are used for validation in this experiment?” Review them in Ronri. Keep longer reasoning and experiment context in the original note instead of squeezing every detail onto one card.
Finish with an application task: explain why a result changed, reproduce a small experiment or compare two conditions. For a web-based study session, choose a short task that fits your device. Ronri is the Apple app described on its product page; this workflow does not imply a native Android app or a built-in coding runtime.
Try it yourself
Your experiment log contains training error but no held-out evaluation. An AI summary calls the model reliable on new data. What should you record?
Show the answer and explanation
Record the observed training result and mark the claim about new data as unsupported by this experiment. Add a task to perform the relevant held-out evaluation.
Separating observation from inference makes the note useful. A confident summary cannot supply an evaluation that was never performed.