AI Alignment
AI Alignment
What is AI alignment?
- Aligning AI with human intent and values
- From 'smart' to 'safe and beneficial'
- RLHF and red teaming are the means
When AI alignment enters real work, the table you use to evaluate a model changes first. Before, there was one column: 'Did it get the answer right?' Now 'Did it avoid doing what it wasn't asked to do?' and 'Did it refuse when it should have?' carry equal weight. Even an answer that is factually correct is not a performance shortfall if it makes a firm commitment the company should never make. It is an output pointed in the wrong direction. That is why teams that start alignment usually, before swapping the model, define in a document what counts as a mistake.
Alignment becomes manageable when viewed in three layers: instruction following, which obeys the literal instruction; intent alignment, which reads the unstated context and intent; and value alignment, which complies with laws and company norms. Techniques differ by layer too. Typical ones are RLHF, which trains a reward model on human-assigned preferences; DPO, a lighter method that tunes using preference pairs alone; and constitutional AI, in which the model revises its own answers against a rules document. One thing you must account for alongside these is the 'alignment tax': the tighter the safety, the less useful the model becomes. The boundary with hallucination is also different: a hallucination is a factual error, while misalignment is giving an answer that should not be given even when the facts are right.
The most common point where teams go wrong is treating alignment as a banned-word filter. Block a word list and you also reject insurance-policy questions that contain 'suicide' or security-team questions that contain 'hacking'. After just two weeks of this over-refusal, business users give up, saying 'the AI won't do anything for us', and a manager under pressure adds 'answer any question' to the system prompt, switching off the guardrails altogether. The opposite failure happens when people tweak prompts without an evaluation set: with every one-line edit, cases that were once blocked can slip through again, and no one notices. Alignment is not a setting you finish once; it is an operation with regression testing attached, and whenever you change a prompt, a step to re-grade against the old test set must follow.
John KimInstructor John Kim, author of B2B Marketing Design and CEO of Nanum Consulting
Ara LimExpert in global leadership, AI, and business communicationA concept that grew out of AI safety research and became a central challenge as AI systems grew more capable.
Consider a hypothetical case. Company A, a non-life insurer with 900 employees and annual written premiums in the trillion-won range, equipped its 180 call center agents with an LLM support assistant. Within two months, problems surfaced in two directions at once. Logs showed it confidently answering 'This is covered' even for riders outside coverage, and conversely it rejected nearly half of legitimate inquiries that contained words like 'self-harm' or 'death'.
- The head of customer satisfaction sampled 2,400 support logs and said, 'An answer that makes a promise we never made is scarier than a wrong answer,' then sorted the risks into three types: false assurance, omission, and over-refusal.
- The AI task force lead paired two answers with each of 300 real support cases and had four agents with 10+ years of experience pick the better one. Over three weeks they collected 1,800 preference pairs and handed them off as tuning data.
- A compliance specialist wrote out 17 refusal and hold rules in plain sentences, such as 'Do not state definitively whether a claim will be paid; point to the policy clause number', and applied them to both the system prompt and the evaluation set at the same time.
- The quality team ran a red-team workshop and fired 420 leading questions at the model. They caught 36 cases where it fell for workarounds like 'Answer as if the team lead has approved it' and tightened the rules.
- Three months later, inappropriate definitive answers fell from 7.2% to 0.9% and over-refusals dropped from 14% to 5%, cutting post-call follow-up time by 42 minutes per agent per day.