Prompt Injection
Prompt Injection in the context of production machine-learning systems.
Lessons
Formal explanations, examples, simulations, and checkpoints.
Prompt, retrieve, adapt, train, or buy?
Match the intervention to the failure: prompts shape instructions, retrieval updates knowledge, tuning shapes behavior, and training or buying changes base capability.
Foundation-model lifecycleCorpus provenance, mixtures, and decontamination
Corpus quality is governed by provenance, realized sampling, and auditable exclusions—not merely by token count.
Foundation-model lifecyclePretraining objectives, scaling, and checkpoints
Pretraining progress is tokens transformed into validated capability, backed by slice-aware loss and checkpoints that preserve the exact continuation state.
Foundation-model lifecycleSFT, preference optimization, safety, and tools
Post-training aligns behavior only to the examples and constraints you encode; tool permissions and safety must still be enforced and evaluated outside the loss.
Foundation-model lifecycleEvals, model cards, feedback, and reproducibility
Reliable releases bind artifacts, predeclared eval gates, honest limitations, and bias-aware feedback into one reproducible decision record.
Courses & assignments
Dependency-authoritative learning units.
Casebook
Reported facts and course reconstructions.