Decoders and generation on the How to Make AI Models track. A decoder predicts the next token given previous tokens. Errors compound. Evaluation is harder than accuracy on a fixed label.
This lesson assumes you already worked through Encoders.
The idea in practice
Use teacher forcing carefully and evaluate with the metric of the product, not only next-token loss.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'Decoders and generation',
}
checks = [
'input available at decision time',
'score matches the real decision',
'failure case written down',
]
print(goal['lesson'])
for item in checks:
print('-', item)
Run the sketch locally if you have Python. The printout is a reminder of the checks, not a trained model. Replace the strings with the real inputs from your own example before you treat it as a design.
What usually goes wrong
A low next-token loss that still produces useless answers. When this happens, stop adding parameters or tools. Fix the check, the data, or the permission, then run the same example again.
What to write down
- The input you are allowed to use at decision time.
- The output and the score or pass rule.
- One failure you will test on purpose.
- What you will not claim the system can do.
Practice
Name one product metric you would report besides loss.
Self-check
- Say Decoders and generation in one sentence that mentions an input and an output.
- Name the failure mode in this lesson and the check that would catch it.
Done when: you can explain this lesson without the page open, and you have a written failure case.