AI alignment is the challenge of ensuring that AI systems act in accordance with human values, intentions, and goals. Misaligned AI might technically complete tasks while causing unintended harm.
Alignment Challenges
- Specification gaming: AI finds loopholes in instructions
- Reward hacking: Optimizing metrics without achieving intent
- Goal misgeneralization: Correct behavior in training, wrong in deployment
- Deceptive alignment: Appearing aligned while pursuing other goals
Practical Alignment for LLMs
- Clear, specific system instructions
- RLHF (Reinforcement Learning from Human Feedback)
- Constitutional AI approaches
- Output monitoring and correction
- Guardrails for policy enforcement
How do I know if my AI is aligned?
Monitor outputs for policy violations, unexpected behaviors, and user feedback. Track metrics like hallucination rate and toxicity to detect misalignment.
Practical example
A system is instructed to answer honestly, including when the available documents do not answer a question.
What to check
Test conflicting incentives, ambiguous requests, and whether the system acknowledges missing evidence.
Limits and tradeoffs
Success on a small behavior test does not establish alignment for every setting.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.