AI alignment

AI alignment is the broad term for the work of making AI systems do what we actually want them to do — not just what we literally asked for, and not just what the training signal rewards.

Modern alignment work spans three loose levels:

• Behavioral alignment — does the model refuse harmful requests, avoid bias, stay on-task, follow instructions accurately? This is the day-to-day work of post-training: RLHF, Constitutional AI, DPO, red-teaming.
• Truthfulness — does the model say what it actually knows (and admit what it doesn't)? Hallucination reduction sits here.
• Long-term/intent alignment — for highly capable systems, will the model pursue the goal you described, or will it pursue a proxy that looks like your goal? This is the more theoretical/safety-critical end of the field, often associated with concerns about future powerful AI systems.

The big AI labs (Anthropic, OpenAI, DeepMind) all have substantial alignment teams. The work shows up in product as:

• Safety classifiers at API boundary — refuse certain categories of request.
• Constitution / spec — written documents that define what the model should/shouldn't do.
• Refusal training — the model learns to push back on unsafe requests instead of complying.
• Steering — techniques to make model behavior more predictable and controllable at runtime.

Alignment is also why model behavior changes over time, sometimes invisibly. The same model name (Claude 3.5 Sonnet, GPT-4o) can be quietly retrained — usually safer, more polite, sometimes less capable on specific edge cases. Production AI systems should evaluate their prompts periodically because the underlying model is a moving target.