How and Why Soft Prompts Are Slowly Replacing Text Prompts

It is rare for a word that originated in the first half of the 14th century to gain a new meaning these days and become a highly popular phrase in almost every language. However, this is exactly what has happened to the word “prompt”, which is now primarily used to refer to instructions and commands for generative models (better known as AI models or Large Language Models, click to read more about LLM use cases).

Currently, most people — chances are you are part of that group as well — think of prompts as text-based entries created in a human language. But there are other types of prompts already being used that are much more sophisticated and, as you would expect, very powerful.

Companies are focusing on advancing AI innovations. According to reports from the Bank of England, the European Banking Authority, and the US Department of the Treasury, 75% of British companies, 85% of EU companies, and 78% of American companies actively use some form of AI in their operations. 

What’s the catch? They are not human-readable. Read on to learn more.

What are soft prompts?

Generally speaking, soft prompts can be described as a concept that involves incorporating vectors into an input sequence and then fine-tuning these vectors while keeping the rest of the pre-trained model’s components unchanged.

So, to create a soft prompt, you need to adjust these vectors while keeping most of the pre-trained weight static. The modified input sequence, defined by the fine-tuned vectors, will then guide the model’s behavior for a particular task.

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What makes soft prompts particularly intriguing is their design, which is very difficult for humans to interpret.

Unlike traditional human-readable prompts, which provide clear instructions expressed in human languages, soft prompts involve incorporating vectors that are very much abstract and random. In other words, these vectors lack a direct linguistic or semantic connection to the task at hand. Therefore, even though these vectors guide the model’s behavior, their non-linguistic nature makes it very hard for humans to understand them.

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How do soft prompts work?

To grasp the fundamental idea behind soft prompting, let’s examine how a model comprehends a prompt like “A famous actor playing a guitar”.

Firstly, the prompt is dissected into individual tokens: “A”, “famous,” “actor,” “playing,” “a” and “guitar”.

Subsequently, all tokens, one by one, are converted into vectors of values, which can be thought of as model parameters.

Then, the model itself can be further adjusted (or trained, to be more specific) by adjusting the values.

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Once we begin changing the weights, the token vectors will no longer match the real vocabulary-based meanings. This is exactly why soft prompts are so difficult to interpret by humans.

The 4 differences between soft prompts and ‘regular’ prompts

1. Approach

When working with regular prompts, the process involves providing the model with a specific input, and in response, the model generates an output based on its pre-existing knowledge and contextual understanding.

The concept of soft prompts takes a slightly different path. In this case, the focus is on modifying the prompt itself without making changes to the core knowledge of the main model. Essentially, soft prompts endeavor to fine-tune only the prompt, rather than the entire model.

2. Flexibility

Crafting regular prompts requires careful consideration for each specific task to achieve optimal outcomes.

On the other hand, soft prompts are flexible and can be easily changed for different tasks. You can do this without completely changing the whole model, which provides a lot of flexibility in how you handle tasks.

3. Task Adaptation

Regular prompts are usually customized for specific tasks, which could mean making unique prompts or even building different models for each task to get the best outcomes.

That said, soft prompts are highly adaptable. By tweaking prompts, you can use the same main model for various tasks, making it easy to switch between tasks without any disruptions.

4. Token Length

Regular prompts can be really long, especially if the task is complex. Soft prompts are more concise in the number of words they use. This is especially true when they’re used for many tasks with the same model. This precision can make it much easier to handle tasks and improve how they perform.

Read more; LLaMA vs ChatGPT: Comparison

Hard prompts and soft prompts — what’s the difference?

Hard Prompts involve providing specific and explicit instructions or queries to the model. They are crafted in a human-readable format and are designed to directly guide the model’s response.

When using hard prompts, a model’s behavior is directly controlled by the input instructions — although some fine-tuning may be required to achieve optimal performance on different tasks.

So, the difference between hard prompts and soft prompts is that hard prompts provide explicit instructions to guide the model’s responses and might require tailored models for different tasks, while soft prompts offer a more versatile and efficient strategy by adjusting prompt parameters to guide a model’s behavior across various tasks.

What are the applications of soft model prompting?

One of the biggest benefits of soft prompts is that they make multi-task learning easier. Unlike traditional methods that require separate adjustments for different tasks, soft prompts let a single model switch between tasks smoothly just by changing the prompts. By utilizing this method, it is possible to save both time and resources, while also preserving the model’s knowledge.

Soft prompting has a wide range of applications, including analyzing sentiments, answering questions, translating languages and summarizing texts.

In the case of chatbots and conversational agents, for example, soft prompts offer a particular advantage. By tweaking prompts, these agents can customize their responses for different personalities or styles and create more engaging interactions.

What are the latest trends in the application of AI technology in B2B sectors? 

The latest trends show a shift towards generative AI, hyper-automation, and AI-driven data analytics, all aimed at creating new value and disrupting traditional business models. 

Financial services 

AI is revolutionizing operations in the B2B financial services sector by enhancing security, personalizing client interactions, and streamlining processes. Hyper-personalization at scale is a key trend, with AI algorithms analyzing vast datasets to offer business clients bespoke financial products and advice. AI-powered platforms are also at the forefront of fraud detection and risk management, employing machine learning models to identify anomalies in real-time. Furthermore, intelligent automation transforms back-office functions like underwriting and compliance, significantly reducing manual effort and improving accuracy. 

Telecom

For the B2B telecom industry, AI is critical for managing complex networks and delivering superior connectivity services. Powered by AI, predictive network maintenance allows providers to anticipate and resolve issues before they impact business customers. AI is also instrumental in network optimization, dynamically allocating resources to ensure quality of service. The rise of 5G and IoT has further accelerated AI adoption, enabling a new suite of B2B services, from smart city infrastructure management to private enterprise networks. 

Biotech & life sciences

The influence of AI in the biotech and life sciences B2B sector is particularly significant, as it accelerates the pace of discovery and development. AI is being utilized in drug discovery, which helps shorten timelines by identifying potential drug candidates and predicting their effectiveness. In clinical trials, AI is enhancing patient recruitment and data analysis. B2B platforms increasingly provide AI-powered tools for genomic analysis and personalized medicine, allowing healthcare providers to deliver more targeted treatments. 

Media & entertainment

Within the B2B media and entertainment sector, AI is transforming content creation, distribution, and monetization. Generative AI is being used to create and augment content, from scripts to visual effects. AI-driven content personalization and recommendation engines are enabling B2B platforms to deliver highly relevant content to their business partners and audiences. Moreover, AI is optimizing advertising strategies through predictive analytics, ensuring that marketing efforts are targeted and effective. 

Soft prompts in manufacturing and real estate  

Soft prompts are a highly effective tool for tackling complex AI-driven tasks in manufacturing and real estate sectors. Soft prompts’ greater adaptability and nuance are crucial when dealing with the multifaceted challenges inherent in manufacturing and real estate industries. 

Manufacturing: Optimizing complex processes

In manufacturing, soft prompts excel at optimizing intricate processes. For instance, companies can be used them to fine-tune robotic arms for delicate assembly tasks, where precise movements and adjustments are critical. By learning from vast datasets of successful operations, soft prompts can guide the AI to achieve a higher degree of accuracy and efficiency than what could be achieved with pre-programmed instructions alone. 

Another key application is in predictive maintenance. Soft prompts can analyze sensor data from machinery and identify subtle patterns that indicate an impending failure. This allows for proactive maintenance, reducing downtime and costly repairs. The adaptability of soft prompts is particularly valuable in this context, as they can learn to recognize new and unforeseen issues as they arise. 

Real estate: Enhancing client interaction and analysis

Soft prompts transform how professionals interact with clients and analyze market data in the real estate industry. For example, AI-powered chatbots can use soft prompts to engage in more natural and helpful conversations with potential buyers. Instead of simply providing pre-written answers, these chatbots can understand the nuances of a buyer’s query and provide more personalized and relevant information. 

Soft prompts are can be used by companies to improve property valuation and market analysis. AI models can deliver more accurate and insightful valuations by training on extensive datasets that include property information, market trends, and economic indicators. This enables real estate professionals to make better-informed decisions and offer more effective advice to their clients. 

Soft prompts in courier software and sports betting

AI-driven personalization and real-time problem-solving make soft prompting optimal for courier software and sports betting sectors.   

Courier software: Proactive customer support

In the courier sector, customer queries are often complex and time-sensitive, involving variables like failed delivery attempts, customs issues, or rerouting requests. Traditional chatbots struggle with these non-standard issues. Soft prompts can empower AI support systems to handle such complexities with human-like nuance. For example, instead of a rigid “track my parcel” function, a soft-prompt-tuned model could interpret a customer’s message like, “I won’t be home for the delivery this afternoon, can the driver leave it with my neighbor at number 22 if I’m not there?” The AI could then verify security protocols, check driver feasibility in real-time, and confirm the alternative arrangement, transforming a potential issue into a seamless customer experience. This moves support from reactive status updates to proactive problem resolution. 

Sports betting: Interactive engagement

The sports betting industry can leverage soft prompts to create highly dynamic and interactive user experiences beyond static odds displays. Imagine a live betting platform where an AI, guided by soft prompts, generates real-time, narrative-driven commentary and personalized betting suggestions based on unfolding events in a match. For instance, if a star player seems fatigued, the AI could generate a prompt like, “Notice Player X’s sprint speed has dropped 15% this half. Odds on the opposition to score next have just shifted. Interested in a tailored in-game bet?” This creates a more engaging, almost conversational, betting environment, turning passive observation into an interactive experience and fostering deeper user engagement by personalizing the action to individual interests and live-game context. 

Soft prompts use cases for companies

Various companies can take advantage of soft prompts’ potential. For the following few paragraphs, let’s focus on potential scenarios that could become reality.  

Sports betting and real-time engagement:

DraftKings can utilize soft prompts to generate hyper-personalized, real-time marketing and betting suggestions. Instead of sending generic “bet on the game” notifications, they can use soft prompts tuned on a user’s betting history and live game data. For instance, if a user frequently bets on a specific basketball player to score three-pointers, and that player is performing well, a soft-prompt-driven AI can instantly craft and send a push notification like, “LeBron just hit his third three-pointer tonight! The odds for him to score over 5.5 threes are now +150. Place your live bet now.” This level of dynamic personalization, created at scale, significantly increases user engagement and in-game spending, directly boosting revenue.w 

Manufacturing and quality control:

In manufacturing, a company like Siemens can use soft prompts for visual quality control systems. A standard AI model might be trained to detect defects in a product line. Instead of a costly complete model retrain, a soft prompt can be quickly tuned with a few examples of the new defect. The system learns to identify this specific anomaly without forgetting its general knowledge. This rapid adaptation reduces waste by catching defects earlier, requires fewer manual inspection hours, and improves product quality, safeguarding revenue and reducing production costs. 

Customer service in financial services:

A financial institution such as Morgan Stanley can deploy soft prompts to refine its internal knowledge base chatbots. Analysts often have particular, complex queries about financial regulations or market data. A general AI might provide broad answers. The chatbot can provide precise, context-aware answers by tuning a model with soft prompts trained on internal compliance documents and successful query resolutions. This drastically cuts down the time analysts spend searching for information, increasing their productivity and enabling faster, more informed trading or advisory decisions, directly impacting financial outcomes. 

What is the difference between prefix tuning and soft prompt tuning?

As you can guess, soft prompt tuning and prefix tuning are two different techniques used in machine learning to fine-tune models for specific tasks. Both have their strengths and weaknesses, and the choice of which to use will depend on the specific case and desired outcomes.

Prefix tuning involves adding a specific prefix to input text to guide the model towards generating more accurate outputs. This technique is useful when the desired outcome is to generate text that is highly relevant to a specific topic or context.

And so, without having to explain what soft prompt tuning is at this point, we can say that the main difference between prefix tuning and soft prompt tuning is that prefix tuning is used to generate something that is more accurate with the concept behind the prompt, while soft prompt tuning is about creating more diverse outputs based on a more general prompt.

What is the difference between soft prompts and LoRA?

Soft prompts and LoRA are different technologies that are used in two different ways.

The LoRA technique involves understanding a matrix that represents the rank composition, which is created by reducing the weight matrix in a transformer.

Conversely, prompt tuning relies on using a soft prompt that is encoded within the model to be learned, rather than relying on a predefined hard prompt given by a person for the task.

Even though both methods can be helpful in model learning, prompt tuning is generally considered more effective.

Soft prompts and constant learning and adaptability  

The fields demanding constant attention are rapidly evolving. Key research areas include few-shot and zero-shot learning, where models are trained to perform tasks with minimal to no specific examples, making AI deployment faster. Another critical area is model interpretability and explainable AI (XAI), which are essential for debugging, ensuring fairness, and meeting regulatory requirements in sensitive sectors like finance and biotech. Furthermore, research into efficient model architectures and new prompt-tuning techniques continues to push the boundaries of what’s possible with less computational power. Staying current with publications and open-source projects in these domains is crucial for anyone building or deploying AI solutions. 

The changing environment presents significant development opportunities. For developers and AI specialists, this translates to an increasing demand for skills in prompt engineering, AI ethics, and optimizing models for specific hardware. For businesses, the potential lies in creating innovative services that were once unimaginable. For example, this could involve developing hyper-personalized interactive experiences in media, creating AI-driven diagnostic tools in life sciences, or building proactive maintenance systems in manufacturing. By cultivating an adaptable workforce and investing in research and development, companies can transform these technological advancements from potential threats into powerful competitive advantages, securing their position at the forefront of the market. 

Final thoughts regarding soft promts

Some say that soft prompts are driving a big revolution in the AI landscape by offering a highly dynamic approach to task guidance

Unlike the ‘traditional’ prompts that are based on explicit instructions, soft prompts involve adjusting prompt parameters while preserving the core message. This flexibility empowers a single model to efficiently perform multiple tasks, which reduces the need for extensive fine-tuning or the creation of other models.

Taking into account that AI has already accelerated the work of many business environments around the world, we can expect that soft prompt tuning — contrary to what its name suggests — will have a strong impact on the development of AI-based tools and on how prompt creators will use those tools. It’s only a matter of time before you’ll hear this term more often in conversations about large language models. To learn more about AI development opportunities, fill out the contact form.

FAQ section

What is the difference between hard and soft prompts? 

The primary difference between hard and soft prompts lies in how they are created and what they are made of. A hard prompt is the standard, human-readable text we manually write to instruct an AI model, such as “Summarize this article in three sentences.” Its effectiveness relies on our ability to find the perfect wording through trial and error, a process known as prompt engineering. 

In contrast, a soft prompt is not made of actual words but is a collection of numerical values, or an embedding, that the AI learns automatically. Instead of a person trying to find the best instructions, the model is trained to generate its own optimized prompt vector for a specific task. This machine-learned prompt is often more effective and efficient, as it communicates with the model in its preferred mathematical language. Essentially, hard prompts are human-crafted instructions, while soft prompts are AI-generated instructions, allowing for a more direct and nuanced way of guiding the model’s behavior without needing to retrain the entire system. 

What is the difference between fine-tuning and prompting LLM?

Fine-tuning and prompting are two distinct methods used to modify and control large language models (LLMs). Fine-tuning involves training a pre-trained model on specific tasks or datasets, which make it more specialized for particular applications. It refines the model’s parameters to improve performance on specific tasks. The term prompting describes providing input text, also known as prompts, to direct the model’s output (answers). Users can influence a LLM’s responses by carefully crafting prompts without altering the existing model. Fine-tuning a model requires having access to its architecture, as well as a significant number of computational resources. At the same time, prompting is a more accessible and user-friendly approach for most applications, as it enables users to interact with a model without the need for extensive training or reprogramming.

Read also: What are LLM hallucinations?

How do soft prompts ensure precision and accuracy in AI-generated outputs?

Soft prompts are crucial in ensuring precision and accuracy in AI-generated outputs by guiding a model’s response in a specific direction. Unlike so- called hard prompts, which strictly define the input context, soft prompts provide general guidance while giving a model some flexibility in interpretation. This available space enables a model to generate more precise and accurate responses because it can adapt its output based on the context provided by the soft prompt. Soft prompts guide the model’s attention to focus on input aspects aligned with user intentions. By balancing guidance and flexibility, soft prompts empower users to influence the output without setting strict rules, which result in more contextually accurate generative AI responses.

Can soft prompts be combined with other AI optimization techniques?

Soft prompts can be combined with various AI optimization techniques to enhance the overall performance and precision of AI-generated outputs. Soft prompts can guide model responses based on feedback, thereby enabling iterative learning and continuous improvements in accuracy and relevance when integrated with techniques such as reinforcement learning. Combining soft prompts with solutions such as active learning allows a model to select informative examples for training, which enhances its understanding of specific contexts. Employing soft prompts alongside adversarial training approaches strengthens a model’s resilience against diverse inputs, ensuring robustness and accuracy in different scenarios. Developers can create more adaptable AI systems by synergizing soft prompts with optimization techniques tailored to user needs and specific applications.

What industries or sectors can benefit the most from soft prompting techniques?

Prompting techniques can benefit several industries or sectors, particularly those using natural language processing (NLP) and human-AI interaction. Let’s name a few, starting with customer support and services, where soft prompts enable an in-depth understanding of user queries, which enhance chatbots and customer interactions. The content creation industry can improve its output by generating customized articles, promotional materials, and creative content, while ensuring texts are free of any spelling, grammatical, or punctuation errors. Additionally, online education platforms can provide tailored learning experiences that cater to students’ individual needs. The legal and research sectors can use data analysis and contextual information extraction to their advantage. In addition, including soft prompts in gaming and user interfaces can enhance human-computer interaction, resulting in more engaging and responsive experiences.