Ready
The response separates observed values from architectural expectations, pins model and tokenizer revisions, defines representative scenarios, reports uncertainty and failures, and sets a decision threshold.
Lesson 10 · Explain and evaluate
Training target revealed
The animal did not cross the street because it was too tired
01 · Smallest useful mechanism
A useful mental model predicts what changes before a run—and identifies what cannot be known without one. Replace “animal” with “robot.” The text changes immediately. Token IDs and sequence length may change, depending on the tokenizer. Different IDs select different initial vectors. From there, attention patterns, residual states, logits, probabilities, and the generated token are model-dependent results that require inference to know exactly. Decoding settings can also change the selected token from the same distribution. The learned model weights do not change during ordinary inference. This trace turns transformer theory into a debugging tool. The final step turns it into an evaluation plan: define the behavior the product needs, test representative and failure cases, and report more than one convenient average.
A prompt edit changes runtime representations; learned weights stay fixed during inference, and product quality still has to be measured.
02 · Experiment
Learner trace · evidence ledger
Reconstruct the pass from earlier observations, including the pinned GPT-2 snapshot, and distinguish captured model evidence from exact teaching calculations.
Intervention
For every row, name whether your evidence comes from a hand calculation, the GPT-2 course trace, or a local Bonsai comparison.
03 · Production-minded practice
Commit to a written claim and evidence plan. The Lab stores the draft locally and never sends it to a server.
Your learning artifact
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Capstone decision rubric
The response separates observed values from architectural expectations, pins model and tokenizer revisions, defines representative scenarios, reports uncertainty and failures, and sets a decision threshold.
The response names pipeline components but omits provenance, decoding controls, held-out cases, subgroup analysis, failure severity, or system-level evaluation.
The response treats attention as a complete explanation, a benchmark average as product proof, or fluent output as evidence of factuality, authorization, or hidden reasoning.
04 · Check your understanding
Next: Continue into How Machines Learn for neural-network foundations, enter the AI Agents course to evaluate a complete tool-using system, or rerun this trace with a new prompt.