What is AI Alignment?
The work of making AI systems pursue the goals and values their users and builders intend.
Definition
AI alignment is the work of making sure an AI system reliably does what its builders and users actually intend, and avoids behavior they would consider harmful. An aligned model follows instructions helpfully, stays honest about what it knows, and refuses clearly dangerous requests. Alignment covers both technical training methods and the harder question of whose values and rules a model should follow.
How it works
Alignment mostly happens after a model has been pretrained on large amounts of text. Developers fine-tune it on examples of good answers and then use feedback to reward preferred behavior. A widely used method is reinforcement learning from human feedback (RLHF), where people rank model responses and the model is trained toward the higher-ranked ones. Related methods such as direct preference optimization (DPO) learn from the same kind of preference data more directly, and some developers also train models against a written set of principles. Before release, teams test models through red teaming, deliberately trying to provoke harmful or deceptive output, and add filters and system prompts as further guardrails.
💡 Example
Ask an unaligned text model how to break into a neighbor's email account and it may simply continue the pattern and list steps. An aligned assistant recognizes the request as harmful, declines, and may suggest a legitimate alternative, such as how to secure your own account. The same training also makes it more likely to admit uncertainty instead of inventing an answer.
Why this matters
Alignment decides whether an AI tool is safe and dependable to rely on, not just capable. Poorly aligned models can follow malicious instructions, flatter users with wrong answers, or take unwanted actions when connected to real systems. As AI agents gain access to email, files and payments, alignment becomes a practical concern for every team choosing a tool, not only for researchers.
Tools that use this concept
ToolChase reviews of these tools cover their safety focus or alignment methods such as RLHF.
Related concepts
When an AI model generates plausible-sounding but factually incorrect information.
An attack that hides instructions in text an AI reads so it ignores its original instructions.
Training a pre-trained AI model on specialized data to improve performance on specific tasks.
Explore AI tools
Find tools that use AI alignment in practice.
What is AI Alignment?
AI alignment is the work of making sure an AI system reliably does what its builders and users actually intend, and avoids behavior they would consider harmful. An aligned model follows instructions helpfully, stays honest about what it knows, and refuses clearly dangerous requests. Alignment covers both technical training methods and the harder question of whose values and rules a model should follow.
How does AI Alignment work in practice?
Ask an unaligned text model how to break into a neighbor's email account and it may simply continue the pattern and list steps. An aligned assistant recognizes the request as harmful, declines, and may suggest a legitimate alternative, such as how to secure your own account. The same training also makes it more likely to admit uncertainty instead of inventing an answer.
What is RLHF?
Reinforcement learning from human feedback (RLHF) is a training method in which people compare or rank model responses, a reward model learns those preferences, and the language model is then tuned to produce answers that score higher. It is one of the most common ways chat assistants are aligned after pretraining.
Is AI alignment the same as AI safety?
They overlap but are not identical. AI safety is the broader field of preventing harm from AI, including misuse, security and reliability. Alignment is the part focused on whether a system's goals and behavior match what its developers and users intend.
Can an aligned AI model still make mistakes?
Yes. Alignment reduces harmful or unwanted behavior but does not guarantee correct answers. Aligned models can still hallucinate facts, misread instructions or be manipulated by prompt injection, so important outputs should still be checked by a person.