This is not my usual dev.to content BUT sometimes you just have to share what excites you.
The lecture clearly separates Pre-training from Post-training — and this distinction is critical for enterprises.
Pre-training basically teaches the model to predict the next word based on massive, unfiltered internet data. It has zero understanding of corporate policies, data protection, compliance or ethics.
Real business value and safety only emerge in Post-training.
Especially through techniques like RLHF (Reinforcement Learning from Human Feedback). This is where guardrails, alignment, and security controls are built.
As someone transitioning into System Integration with a strong cybersecurity background, this lecture reinforced my view:
AI alignment and governance are not just “nice-to-have” features.They are core components of modern IT security and risk management.
In a world where companies are rushing to integrate LLMs, weak post-training and missing governance will lead to massive data leaks, compliance violations, and security incidents. Solid infrastructure is essential, but secure and governed AI systems are what actually make AI usable in regulated environments.
We need to treat Generative AI as the ultimate infrastructure and security stress test.
What’s your take?
How is your organization handling AI governance and security in 2026?
Sources & Lecture:
Stanford CS229: Building Large Language Models
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