Can we change how a large language model responds without retraining it, fine-tuning its weights, or rewriting the prompt?
This short technical video introduces activation steering—an experimental technique that modifies selected internal model activations during inference.
You will learn:
• Fine-tuning versus activation steering• How model activations can be modified at runtime• How positive and negative examples create a steering vector• How steering strength influences the result• Why steering can improve some answers and degrade others• Whether activation steering is ready for production
The demonstration includes both a successful result and a case where the baseline model performs better. This shows an important lesson: changing an LLM’s behaviour does not always mean improving it.
Activation steering is currently an experimental research technique. Its effectiveness depends on the selected model layer, input examples, steering direction, and coefficient strength.
🎥 Watch the complete Tamilboomi workshop here:
https://youtu.be/WK7HgiaBWXY
The full session includes detailed explanations of model training, fine-tuning, adapter layers, matrix decomposition, activations, steering vectors, live demonstrations, student questions, and the different stages of LLM training.
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