TL;DR
A technique for controlling an AI model's behavior at inference time by directly modifying the internal activation vectors of its intermediate neural network layers.
Activation engineering alters the model's output without modifying its underlying weights or relying solely on prompt instructions. By identifying specific vectors in the network's latent space that correspond to particular concepts or styles, engineers can inject or suppress these values during the forward pass. This allows for real-time control over model attributes, such as tone, truthfulness, and safety constraints.
Why this matters for your business
It offers a fast, post-training approach to guide and secure AI behavior during runtime, circumventing the need for expensive fine-tuning.
