Soft Prompt

A Soft Prompt is a learned vector embedding that conditions a frozen language model toward a specific task — distinct from hard prompts which use discrete tokens (actual words). Soft prompts live in the model's embedding space rather than its vocabulary; they may not correspond to any real words but they activate the model's behavior in useful ways. Soft prompts are typically 1-100 tokens long and require gradient-based training (just like fine-tuning the model), but they update only the soft-prompt parameters — leaving the underlying model unchanged. The approach was pioneered by Google's prompt-tuning work in 2021 and demonstrated dramatic efficiency gains over full fine-tuning. Variants include prefix tuning (which adds learned vectors to every layer of attention) and P-tuning (Liu et al., which uses a small neural network to generate the soft prompt). AI governance, AI compliance, and AI risk management programs document soft-prompt artifacts as separate governed assets — supporting responsible AI through transparent tracking of model customizations in enterprise AI deployments.

Centralpoint Catalogs Soft Prompts Alongside Hard Prompts: Oxcyon's Centralpoint AI Governance Platform tracks every prompt artifact — hard or soft — across OpenAI, Gemini, Llama, and embedded models. Centralpoint meters consumption, keeps prompts and skills on-prem, and embeds adapted chatbots into your portals via one JavaScript line.


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