AHands-on reviewed
Anthropic
Anthropic Prompt Engineering Interactive Tutorial
For: Anyone whose primary LLM is Claude (or who builds with Anthropic API)
Beginner · ~9 chapters (3-6 hours) · Free
Best AI safety courses (2026)
AI safety has moved from niche concern to baseline engineering hygiene. These are the courses we recommend.
AI safety in 2026 has bifurcated into two distinct curricula. Applied AI safety covers the production-engineering concerns: prompt-injection defense, output filtering, jailbreak resilience, evaluation against adversarial inputs, human-in-the-loop guardrails. Foundational AI safety / alignment covers the research-tier concerns: scalable oversight, RLHF evaluation, interpretability, dual-use risk. Both are legitimate; very few engineers need both.
For engineers shipping AI features, the applied curriculum is non-negotiable in 2026. The cost of shipping an unguarded LLM into production has risen materially — both the practical cost (prompt-injection incidents at major SaaS companies cost millions in 2025) and the regulatory cost (the EU AI Act, ISO 42001, and emerging US state-level rules). The courses on this page focus on applied safety primarily, with one alignment-research entry for engineers considering frontier-lab roles.
The honest framing on free vs paid: the best AI safety material in 2026 is free, published by Anthropic and OpenAI as part of their developer education stack. The paid courses (Coursera, edX) are slower to update and less hands-on. If you take only one resource on this page, take Anthropic's Claude safety documentation.
Pre-requisites
Before taking these courses, make sure you understand: Hallucination, RLHF.
Recommended courses (1)
Filter all ai safety courses →Frequently asked questions
What's the difference between AI safety and AI security?+
AI security is the cyber-security framing: defending models and their deployments from attackers (prompt injection, data exfiltration, model theft). AI safety is broader: making sure the model itself doesn't cause harm to users or third parties (jailbreaks, hallucination at high stakes, bias amplification, dual-use risk). Most production teams need both; engineering teams often start with security and add safety as the deployment scales.
Do I need to learn AI safety if I'm just building a chatbot?+
Yes — at minimum the applied basics. Prompt injection alone has caused multiple high-profile incidents at shipped SaaS products in 2025-2026. The minimum bar in 2026: understand prompt injection, implement output validation, never trust LLM output that touches privileged operations without human review.
Where should I start?+
For engineers: Anthropic's safety documentation + the relevant chapters of the Hugging Face LLM course on RLHF. For ML researchers: ARENA (the Alignment Research Engineer Accelerator, free) and the Anthropic / Redwood / MATS reading lists. For PMs: the EU AI Act risk-tier framework and the NIST AI Risk Management Framework as background.
Related agents & tools
Once you've learned the concepts, these are the agents and tools where the skills pay back.
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