AI foundations with Brilliant: turn concepts into a checkable workflow

Understand the kind of output an LLM produces

Thanks to Brilliant for sponsoring this video.

The 30 December 2025 lesson looks ahead to learning AI in 2026. It uses interactive visuals and Brilliant courses to discuss next-token prediction, probability, temperature and hallucinations. The practical question is how to work with uncertain generated output rather than treating a fluent response as a guaranteed answer.

1. Follow the model explanation at 1:59 and the temperature discussion at 3:25 before interpreting the workflow examples.
2. Choose a small question with an answer you can verify and compare generated responses with the evidence.
3. Record errors and uncertainty rather than assuming a change in randomness establishes truth or removes hallucinations.

Break one real job into smaller decisions

At 4:53 the recording introduces three fundamentals, then connects basics to workflows at 6:52. The description names decomposition, reasoning under constraints and clear inputs, outputs and guardrails. Use a narrow task to see where a model judgement belongs and where a fixed rule or a human review is more appropriate.

1. Write the input, desired result and limits of one task in ordinary language.
2. Split the job into smaller steps with an observable intermediate result after each one.
3. Identify what the model may infer, what must come from a source and where it should stop for review.
4. Try a normal case and a missing-information case, keeping the resulting behaviour separate from what the system was merely instructed to do.

Choose a learning path that supports the actual gap

Brilliant learning paths appear at 5:46 and systems thinking at 7:39. The source points to AI, neural networks and “thinking in code” rather than prescribing a new framework for every problem. Choose study from the concept that blocked your small workflow, then apply it back to the same example.

1. Use the retained resource: https://brilliant.org/BrilliantAILuke/.
2. Compare the course coverage with your uncertainty about probability, decomposition or coding logic, and keep a worked example as evidence of learning.
3. Review the workflow again after the lesson and explain what changed in its assumptions or checks.
4. Check current eligibility separately. The recorded 30-day access and 20% annual Premium offer are historical terms, not confirmed current discounts.

The takeaway

The useful progression is from understanding uncertainty to designing a small observable system, with tools chosen to serve that system.