AI Safety & Guardrails
For engineers preparing for interviews on building safe LLM products, from AI engineers to security and platform roles. You will be able to explain prompt injection, guardrails, PII protection, bias testing, red-teaming, incident response and regulations such as the EU AI Act and India's DPDP Act, with a short interview answer and a real example for each.
Course Content
Prepares you to explain hallucination, grounding, alignment, guardrails, prompt injection, bias, explainability and trust — the ideas almost every AI safety interview opens with.
Prepares you to answer how AI systems protect personal data, which privacy laws apply (GDPR, India's DPDP Act and others), and how to defend models against adversarial, poisoning, supply-chain and copyright risks.
Prepares you to design layered guardrails for agents and high-risk domains, choose between rule-based and model-based filters, and explain the engineering practices — versioning, evals, rollback — that keep AI systems reliable.
Prepares you to answer what you do when AI systems are abused or cause harm — incident response and post-mortems — and how to find and fix bias feedback loops, proxy variables, intersectional gaps, federated-learning poisoning, cultural moderation gaps and environmental cost.
Prepares you to explain AI governance frameworks (NIST AI RMF, ISO/IEC 42001), the EU AI Act's risk tiers and other emerging rules, legal risk and accountability, audit logs, appeals, and when and how humans must oversee AI decisions.