AI Glossary · Foundations
Pre-training vs. Post-training
Pre-training builds general capability on broad data; post-training (SFT, RLHF, DPO) shapes tone, safety and task behavior.
Definition
What is Pre-training vs. Post-training?
Pre-training vs. Post-training is pre-training builds general capability on broad data; post-training (SFT, RLHF, DPO) shapes tone, safety and task behavior.
- Category
- Foundations
- Glossary set
- 28 related terms
- Audience
- Enterprise AI leaders
Why does Pre-training vs. Post-training matter in enterprise AI?
Pre-training vs. Post-training matters in enterprise AI programs because it helps business and technology leaders align vocabulary, scope, ownership, and measurable outcomes.
Related terms in Foundations
- Alignment
- The discipline of making an AI system's behavior match human intent, values and organizational policy — both at training time and at runtime.
- Artificial Intelligence (AI)
- Software systems that perform tasks normally requiring human intelligence — perception, reasoning, language, decision-making — by learning patterns from data.
- Artificial General Intelligence (AGI)
- A hypothetical AI capable of performing any intellectual task a human can, across domains. Not a shipping product category today; treat vendor claims with scrutiny.
- Benchmark
- A standardized task suite (e.g., MMLU, GSM8K, HELM) used to compare model quality on reasoning, coding, safety or domain knowledge.
- Chain-of-Thought (CoT)
- A prompting or training pattern where a model produces intermediate reasoning steps before its final answer, improving accuracy on math, planning and multi-hop tasks.
- Classification
- A supervised learning task that assigns inputs to discrete categories (spam vs. not spam, churn risk tier). The workhorse of predictive AI.