What is prompt tuning?

Prompt tuning is a parameter-efficient fine-tuning (PEFT) technique that adapts large pretrained models to new tasks without updating their billions of parameters. Instead, it learns a small set of trainable vectors—called soft prompts or virtual tokens—that are inserted into the model’s input space. These vectors act as continuous signals, controlling the frozen model toward the expected behavior and keep the backbone intact. This perspective significantly reduces compute and storage costs, making it ideal for organizations that need to customize large models across multiple use cases. ^1, 2

Prompt engineering relies on crafting clever text instructions (hard prompts) to elicit the right behavior from a model. While effective in some cases, hard prompts are brittle and hard to optimize at scale. That means in prompt engineering, minor changes in wording can lead to significant and unpredictable variations in performance, which is why it’s difficult to optimize systematically. However, complete fine-tuning updates all model parameters, which is computationally expensive and storage-heavy—especially for models with hundreds of billions of weights. Prompt tuning strikes a balance: it uses continuous embeddings instead of discrete text, trains only these small vectors and achieves performance close to full fine-tuning on many tasks, all while being far more efficient. ^{2, 3}

The prompt tuning approach is based on core components that work together to adapt large pretrained models. The method uses a frozen model, learns a set of soft prompt embedding through gradient-based optimization and is guided by a task-specific dataset. These elements enable parameter-efficient adaptation without retraining the entire model. ^{1, 2}

Pretrained frozen model: A large language model (LLM) or vision transformer works as the backbone. It remains frozen during training, maintaining its general knowledge while reducing compute and storage costs.⁴

Soft prompt embedding: These prompts are trainable vectors also known as virtual tokens attached or inserted into the tokenized input. They act as continuous signals that control the model toward the output task without altering its internal weights.⁴

Task-specific dataset: A labeled dataset aligned with the downstream task is essential for supervised optimization of the soft prompts.

Gradient-based optimization: Only the soft prompt parameters and lightweight head (optionally) are updated by using optimizers, while the backbone remains frozen. This method makes sure that there is efficiency and stability during the task.⁴

As shown in the figure, prompt tuning works by introducing a small set of trainable vectors into the input of a frozen pretrained model. These prompts work as hidden instructions that guide the model toward the target task without updating billions of parameters.

Beyond these core components, several design choices significantly influence performance:

Prompt length: The number of virtual tokens in the soft prompt is a critical hyperparameter. Various researchers performed experiments and derived that the optimal length varies by task. For instance, simple classification tasks might perform best with shorter prompts (for example, under 20 tokens), while complex sequence labeling tasks might require longer ones (for example, around 100 tokens).⁵

Prompt placement: This element optimizes the place of the prompts, whether it appears as a prefix, suffix or interleaved within the input sequence.

Initialization strategy: Starting soft prompts with random values, sampled embeddings or task-specific tokens can impact convergence speed and accuracy.⁴

These additional elements are not mandatory but recommended for achieving optimal results.

Let’s understand the sentiment analysis task that provides a clear explanation of prompt tuning’s mechanics and benefits. Suppose that the goal is to adapt a 175-billion parameter model to classify movie reviews as “positive” or “negative.” A full fine-tuning approach would be prohibitively expensive and slow. With prompt tuning, the process is as follows:

Start with a frozen pretrained model: The 175B parameter backbone remains entirely untouched, preserving its vast repository of general knowledge learned during pretraining.⁵

Add soft prompts: A small set of trainable vectors (for example, 20 virtual tokens) is attached to the input embeddings of every movie review. These vectors are not human-readable text; they are continuous embeddings that exist in the same high-dimensional space as the model’s vocabulary (for example, a 12,288-dimensional space for a model of this scale). Through optimization, these vectors learn to encode a continuous, task-specific signal that steers the model’s behavior.

Feed the input: For example,

[Soft Prompts] The movie was absolutely fantastic!

In this example, suppose that we initialize 20 soft prompt tokens for a sentiment analysis task. After training, the input might look like this internally:

Here, each v1 is a learned, high-dimensional prompt vector. The goal of training is to find the optimal values for the vectors that guide the frozen model to correctly classify the sentiment of the subsequent text.

Train only the soft prompts: By using a labeled dataset of movie reviews, the training process is initiated. Through backpropagation, the error gradient is computed but the optimization step updates only the parameters of the soft prompt embeddings. This approach involves tuning only a few thousand parameters instead of the model’s 175 billion weights.⁵

Deploy with modularity: Once training is complete, the resulting set of 20 vectors constitutes the entire task-specific adaptation. To adapt the same base model for a different task, such as spam detection, one simply trains a new set of soft prompts on a spam dataset and swaps them in at inference time

This technique offers substantial efficiency benefits. Instead of storing a separate, full copy of the model for each task—a 175B parameter model can require up to 350 GB—one needs to store the task-specific prompt parameters, which might be only a few KB in size.¹ This modularity makes prompt tuning a practical and cost-effective solution for large-scale model adaptation.²

Prompt tuning comes with many benefits in real-time applications but it is important to understand its limitations.

The key strengths of prompt tuning is efficiency, modularity and preservation of the base model’s knowledge.

Exceptional parameter and cost efficiency: The most significant advantage is the drastic reduction in trainable parameters. By updating only a small set of soft prompt vectors—often representing less than 1% of the total model, prompt tuning dramatically lowers computational and storage costs. This strategy makes the adaptation of massive foundation models feasible for organizations with limited computational resources.

Modularity and scalable deployment: Because each task is encapsulated in a small, independent set of prompt parameters, a single frozen backbone model can be adapted for numerous tasks simply by swapping these lightweight prompt files at inference time. This “plug-and-play” architecture is highly modular and avoids the need to store and manage separate, multi-gigabyte model copies for every application.

Mitigation of catastrophic forgetting: Full fine-tuning risks overwriting or degrading a model’s pretrained knowledge when learning a new task. By keeping the backbone model’s weights entirely frozen, prompt tuning preserves the vast repository of general knowledge learned during pretraining, allowing the model to be repurposed without losing its core capabilities.

Data efficiency: Compared to full fine-tuning, which often requires large, labeled datasets for each new task, prompt tuning can achieve strong performance with smaller, more modest datasets.

Despite its strengths, prompt tuning is not without its drawbacks, which include limitations in expressive power, training difficulties and a lack of interpretability.

Limited expressive power: A central theoretical limitation is that prompt- and prefix-tuning are less expressive than methods like LoRA or full fine-tuning. Formal analysis has shown that these methods function by adding a bias to the output of attention blocks but cannot fundamentally alter the model’s learned attention patterns. This means prompt tuning is highly effective at eliciting and combining skills already present in the model but can fail to learn truly novel tasks that require new reasoning patterns.

Training instability and hyperparameter sensitivity: One of the most significant practical challenges is the method’s sensitivity to hyperparameters. The training process can be difficult to converge and is highly dependent on the choice of learning rate and prompt initialization strategy and length, often needing careful, extensive tuning to achieve optimal results.

The “black box” problem of interpretability: A major and persistent limitation is the inherent lack of interpretability of soft prompts. Because they are continuous, high-dimensional vectors optimized through gradient descent, they do not correspond to any human-readable text. This “black box” nature makes it difficult to understand what the prompt has learned, why it is steering the model in a certain way and how to debug it when it fails.

Dependency on model scale: The effectiveness of the original, input-level prompt tuning method is correlated with the scale of the backbone model. While it becomes competitive with full fine-tuning on models with over 10 billion parameters, its performance is significantly on smaller, more commonly used models.

The principles of prompt tuning have proven to be highly adaptable, extending far beyond their initial applications in natural language processing. The technique is now a key enabler for efficiently customizing models in multimodal domains, speech processing and for advanced learning paradigms.

Multimodal prompt tuning (vision-language models): Prompt tuning is a critical technique for adapting pretrained vision-language models (VLMs) such as CLIP to downstream visual tasks. In this context, prompts can be engineered for one or both modalities.⁷

Applications in speech processing: The prompt tuning paradigm has been successfully extended to the domain of speech processing. In this application, a raw speech utterance is encoded into discrete acoustic units and a set of learnable, task-specific soft prompts is attached to this sequence. This framework is unified and allows a single pretrained speech model to be adapted for a diverse array of tasks. This includes keyword spotting, spoken intent classification and even automatic speech recognition (ASR), all while training only a small, task-specific prompt.

Multitask and multilingual learning: To further enhance efficiency and generalization, researchers have moved beyond training isolated, single-task prompts. Advanced methods now focus on learning shared prompts that can be transferred across multiple tasks or languages.

• Multitask prompt tuning (MPT): This approach distills knowledge from multiple source tasks into a single, transferable shared prompt. This shared prompt can then be efficiently adapted to new target tasks, requiring as few as 0.035% of the model’s parameters per task and showing strong performance in few-shot learning scenarios.
• Multilingual fine-tuning: Studies on multilingual models have found that multitask fine-tuning on a collection of English-only datasets and prompts can significantly improve a model’s zero-shot performance on tasks in non-English languages. This method demonstrates that the model learns task-solving abilities that are, to some extent, language-independent.

In the research space of artificial intelligence, machine learning and generative AI prompt tuning has emerged as a critical method for the efficient model tuning of AI models. Unlike full model training that alters all model weights and risks over-fitting with limited training data, this technique focuses on optimizing input prompts that are attached to the input text. Through a process of automation and iteration, the goal is to discover an optimal prompt that creates effective prompts for specific tasks, a process whose success is often dependent on the model size. This approach offers a scalable alternative to extensive retraining and complements other strategies like RAG, solidifying its role as a cornerstone for customizing foundation models.