Review Model Steering

Model steering is the use of prompts, constraints, examples, controls, or feedback to guide a model toward desired behavior.

Model steering guides model behavior toward a desired task, style, policy, or constraint. Steering can come from prompts, examples, system instructions, decoding controls, fine-tuning, or feedback.

The steering method affects how durable and reliable the behavior is. A prompt-level preference is different from a trained behavior, and neither guarantees compliance in every context.

A system prompt directs a model to answer as a concise safety reviewer, while examples demonstrate the required format and escalation threshold.

Steering does not prove that a model understands or agrees with the direction. It changes the conditions shaping its output and may fail under conflicting inputs.

Steering shapes behavior; it does not create judgment.

* [[system-prompt|System Prompt]] * [[alignment|Alignment]] * [[guardrails|Guardrails]] * [[fine-tuning|Fine-Tuning]]

* [Long Ouyang and colleagues, “Training Language Models to Follow Instructions with Human Feedback”](https://arxiv.org/abs/2203.02155) — shows post-training methods used to steer language-model behavior toward demonstrated and preferred responses. * [OpenAI, “Model Spec”](https://model-spec.openai.com/) — documents instruction hierarchy and behavioral guidance as system-level steering mechanisms.

* Compare [[system-prompt|System Prompt]], [[alignment|Alignment]], [[guardrails|Guardrails]], and [[fine-tuning|Fine-Tuning]] to place Model Steering in the surrounding system. * Follow the cited source material above for the method, standard, or research context behind this draft.