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Soft Prompting vs. Hard Prompting in AI: Enhancing Control and Creativity

January 19, 2024 | AI

When we start digging into the world of artificial intelligence (AI), all of sudden the concept of “prompting” plays a pivotal role in influencing the behavior of language models and other AI systems. Traditionally, AI models have been guided by hard prompts, which are explicit instructions or questions given to the model to produce specific outputs. However, as AI continues to evolve, there is an increasing need for more flexible and nuanced interactions between humans and machines, and we quickly meet “soft prompting,” a novel approach that offers greater control, creativity, and interpretability in AI systems.

Soft Prompting vs. Hard Prompting?

Soft prompting is an innovative technique that allows for less rigid and more natural interactions with AI models. Unlike hard prompts, which provide explicit instructions, soft prompts offer more subtle cues or guidelines to the model, allowing it to generate responses that align better with human intent.

We will explore the fundamental differences between hard and soft prompting and discuss why the latter is gaining traction in AI research and development:

Instruction Explicitness:

Hard Prompting: Hard prompts involve providing explicit and specific instructions to the AI model. These instructions are clear and leave little room for interpretation. For example, asking the model to “Translate this English text to French” is a hard prompt.
Another hard prompting example: “Calculate the square root of 25.” In this hard prompt, the instruction is clear and specific, leaving no room for interpretation. The model is expected to provide the exact numerical answer, which is 5.

Soft Prompting: Soft prompts, on the other hand, provide more subtle or indirect guidance to the AI model. They often rely on context, cues, or partial information rather than explicit directives. An example of a soft prompt could be “Translate this text into a different language.”
One more mathematical soft prompting example: “Tell me about numbers that have interesting properties when their square roots are calculated.” This soft prompt provides a broader and less specific request, allowing the AI model to generate a variety of responses related to interesting properties of numbers when their square roots are considered.


Hard Prompting: Hard prompts are rigid and inflexible. They constrain the model to follow the given instructions precisely, which can limit creativity and adaptability.
Hard Prompting Example: “Write a poem about love in 10 lines.” This hard prompt confines the AI model to a strict format and length, limiting its ability to explore creative expressions of love.

Soft Prompting: Soft prompts are more flexible and adaptable. They allow the AI model to consider broader context and incorporate user intent into generating responses. This flexibility enables the model to produce more contextually relevant and creative outputs.
Soft Prompting Example: “Create a poem with themes related to emotions and connections.” This soft prompt offers flexibility by encouraging the AI model to explore a broader range of emotions and connections, allowing for more creative and varied responses.

Control vs. Creativity:

Hard Prompting: Hard prompts offer greater control over the AI model’s behavior, ensuring that it adheres to specific guidelines. However, this high level of control can come at the cost of limiting the model’s ability to generate novel or creative responses.
Hard Prompting Example: “Sum the numbers 3, 5, and 7.” The hard prompt leaves no room for creativity, as the model is expected to provide the exact mathematical sum, which is 15.

Soft Prompting: Soft prompts strike a balance between control and creativity. While they provide users with some control over the model’s output, they also allow the model to exhibit creativity within the given constraints. This results in responses that are both guided by user intent and contextually adaptable.
Soft Prompting Example: “Combine the numbers 3, 5, and 7 in an interesting way.” This soft prompt provides some control by indicating that the numbers should be combined, but it also allows the AI model to use its creativity to determine how to combine them in an interesting manner.


Hard Prompting: Hard prompts tend to produce more predictable and interpretable outputs, as the model’s responses are driven by explicit instructions. This can be advantageous in applications where transparency and predictability are critical.
Hard Prompting Example: “Explain the process of photosynthesis in 100 words.” The hard prompt explicitly requests an explanation of photosynthesis, resulting in a response that is highly interpretable and aligned with the given topic.

Soft Prompting: Soft prompts may lead to less predictable outputs because they rely on implicit guidance and context. However, they can also enhance interpretability by encouraging the model to generate responses that align better with human intuition and context.
Soft Prompting Example: “Discuss the role of plants in the ecosystem.” This soft prompt is less explicit and allows the AI model to discuss various aspects of plant biology, which may include photosynthesis as one of the components. Interpretability may vary depending on the model’s response.

Bias Mitigation:

Hard Prompting: Hard prompts can inadvertently amplify biases present in the training data, as they rigidly follow the provided instructions without considering potential bias in those instructions.
Hard Prompting Example: “List famous scientists from history.” The hard prompt, without context, might lead the AI model to generate a list that predominantly includes male scientists, potentially reinforcing gender bias present in historical records.

Soft Prompting: Soft prompts can be designed to mitigate bias by allowing users to frame questions or requests in a way that avoids or addresses biased content. This can help in producing more fair and unbiased AI responses.
Soft Prompting Example: “Provide information about contributions to science by individuals from diverse backgrounds.” This soft prompt encourages the AI model to consider a wider range of scientists from diverse backgrounds, helping mitigate bias by promoting inclusivity in the responses.

In summary, hard prompting involves explicit and rigid instructions to AI models, while soft prompting offers flexibility, adaptability, and a balance between user control and model creativity. The choice between hard and soft prompting depends on the specific application and the desired level of user guidance, interpretability, and creativity in AI interactions. The examples we listed above illustrate how the choice between hard and soft prompting can significantly impact the nature of AI responses, from specificity and creativity to interpretability and bias mitigation.

What are the techniques for Implementing Soft Prompts?

Implementing soft prompts effectively involves various techniques and strategies to guide AI models in generating desired responses while allowing flexibility. Here are some techniques for implementing soft prompts:

  1. Prompt Engineering:

  2. Natural Language Pivots: Use intermediary phrases or natural language pivots to gently guide the AI model towards the desired topic or task without explicitly specifying it. For example, instead of saying, “Translate this text to French,” you can use a pivot like, “Can you help me with this text?”

    Open-Ended Questions: Frame prompts as open-ended questions or requests, inviting the model to provide detailed and informative responses. For instance, “Tell me everything you know about climate change” encourages a comprehensive answer.

    Conditional Language: Employ conditional language to set expectations for the model’s responses. For example, “If possible, explain the concept of quantum physics” suggests that an explanation may not be exhaustive but should be provided if feasible.

  3. Prompt Variations:

  4. Multiple Prompts: Experiment with providing multiple related prompts to the AI model. This allows you to guide the model’s understanding from different angles and may yield more comprehensive responses.

    Gradual Refinement: Start with a broad prompt and gradually refine it based on the model’s initial output. For instance, if you initially ask, “Tell me about space exploration,” you can then follow up with, “Tell me more about Mars missions.”

    Partial Information: Provide partial information or context within the prompt to guide the model’s response. This can be especially useful for generating informative responses. For example, “Given recent climate data, discuss the impact of rising temperatures.”

  5. Fine-Tuning:

  6. Data Collection: Collect and curate a dataset of prompt-response pairs that reflect the desired behavior and use it for fine-tuning the AI model. This dataset should include examples of soft prompts and the corresponding desirable responses. Now, let’s take a look at an example illustrating the process of data collection during fine-tuning for implementing soft prompts:

    Let’s say you are developing a chatbot that provides information about travel destinations. You want to fine-tune the chatbot model to respond effectively to soft prompts related to travel recommendations and facts about various destinations.

    Data Collection for Fine-Tuning:

    1. Gather a Dataset:
    2. Begin by collecting a dataset of soft prompts and the corresponding desirable responses. These prompts should represent a variety of user queries and requests related to travel.

      Soft Prompt: “Can you tell me about popular tourist spots in Paris?”
      Desirable Response: “Certainly! In Paris, some popular tourist spots include the Eiffel Tower, Louvre Museum, and Notre-Dame Cathedral. Would you like more information about any of these attractions?”

      Soft Prompt: “What can you tell me about beaches in Hawaii?”
      Desirable Response: “Hawaii is known for its beautiful beaches. Some of the most famous ones are Waikiki Beach, Hapuna Beach, and Lanikai Beach. Each offers its unique charm and activities.”

      Soft Prompt: “I’m planning a family vacation. Where are some family-friendly destinations?”
      Desirable Response: “For a family-friendly vacation, consider destinations like Orlando, Florida, with its theme parks, or San Diego, known for its zoos and family-oriented attractions.”

    3. Curate Diverse Examples:
    4. Ensure that the dataset includes diverse examples of soft prompts, covering different destinations, types of information requested, and user preferences. This diversity helps the model generalize better.

    5. Include Challenging Cases:
    6. Introduce challenging cases where the model needs to understand nuanced soft prompts or provide information based on implicit context. For example:

      Soft Prompt: “I want to experience a different culture.”
      Desirable Response: “Exploring cities like Kyoto in Japan or Marrakech in Morocco can provide a rich cultural experience with unique traditions and cuisine.”

    7. Human Annotation:
    8. Have human annotators review and assess the responses generated by the model. Annotators should verify if the responses align with user intent, are informative, and exhibit the desired level of creativity and adaptability.

    9. Feedback Loop
    10. Establish a feedback loop with annotators to refine the dataset. Annotators can provide feedback on model-generated responses, helping to improve the quality of responses and guidelines for fine-tuning.

    11. Balancing Positive and Negative Examples:
    12. Ensure a balanced distribution of positive and negative examples. Positive examples represent instances where the model’s response aligns well with the desired outcome, while negative examples capture cases where the model’s responses need improvement.

    13. Iterative Process:
    14. Fine-tuning is often an iterative process. Continue to expand and refine the dataset based on the model’s performance and user feedback to improve the model’s responsiveness to soft prompts.

      In the example described above, data collection involves gathering soft prompts and their corresponding desirable responses, curating a diverse dataset, incorporating challenging cases, and iteratively improving the dataset based on human annotation and feedback. This dataset forms the foundation for fine-tuning the chatbot model to effectively respond to various travel-related inquiries with soft prompts.

    Fine-Tuning Objectives: During fine-tuning, use specific objectives to encourage the AI model to pay attention to certain aspects of the prompt. This can involve reinforcement learning or supervised fine-tuning techniques.

    Let’s say you are developing a language model for a customer support chatbot. You want the chatbot to be more empathetic and understanding when responding to customer inquiries and complaints. Your objective definition would be to encourage the model to generate empathetic and understanding responses. This objective aims to make the model more customer-centric in its interactions.

    So, in your dataset collection you gather a dataset of customer support interactions that include examples of empathetic and understanding responses to customer complaints and inquiries. The dataset consists of pairs of user queries and the corresponding customer support agent responses, like this for example:

    User Query: “I’m frustrated because my order was delayed.”
    Agent Response (Desirable): “I’m sorry to hear about the delay in your order. I understand how frustrating that can be. Let me check the status for you.”

    User Query: “I received a damaged product.”
    Agent Response (Desirable): “I apologize for the inconvenience caused by the damaged product. We’ll assist you in getting a replacement or a refund right away.”

    So, during the fine-tuning, you incorporate the collected dataset into the training process. The fine-tuning objective encourages the model to pay attention to empathetic and understanding language patterns. However, it is important to implement a reward mechanism during fine-tuning, which means the model receives positive rewards for generating responses that align with empathetic and understanding behavior. This reward mechanism helps reinforce the desired behavior. On the other hand, you can also introduce a penalty mechanism, which means the model receives penalties for generating responses that lack empathy or understanding.

    It is important to understand the concept of fine-tuning as an iterative process, which means you continuously evaluate the model’s responses and adjust the reward and penalty mechanisms as needed to improve the model’s empathy and understanding.

  7. Prompt Templates:

  8. Template-Based Prompts: Create templates for prompts that guide users to fill in specific details or parameters. For instance, a template for generating recipes might include placeholders for ingredients, cooking time, and steps.

    Here’s an example:

    Title: [Recipe Title]
    - [Ingredient 1]
    - [Ingredient 2]
    - [Ingredient 3]
    - [Ingredient 4]
    - [Ingredient 5]
    - [Additional Ingredients, if any]
    Cooking Time: [Cooking Time]
    Servings: [Number of Servings]
    1. Start by [First Step].
    2. Next, [Second Step].
    3. Then, [Third Step].
    4. Afterward, [Fourth Step].
    5. Continue by [Fifth Step].
    6. Lastly, [Final Step].
    Enjoy your delicious [Recipe Title]!

    The output of the prompt template would look like this:

    Title: Spaghetti Carbonara
    - 8 ounces spaghetti
    - 2 large eggs
    - 1 cup grated Pecorino Romano cheese
    - 4 ounces pancetta or guanciale, diced
    - 2 cloves garlic, minced
    - Salt and black pepper to taste
    Cooking Time: 15 minutes
    Servings: 2
    1. Start by bringing a large pot of salted water to a boil. Cook the spaghetti according to package instructions until al dente. Reserve 1/2 cup of pasta cooking water, then drain the spaghetti.
    2. While the pasta is cooking, whisk together the eggs and grated Pecorino Romano cheese in a bowl. Season with a generous pinch of black pepper.
    3. In a skillet, cook the diced pancetta or guanciale over medium heat until it becomes crispy and browned, about 4-5 minutes. Add minced garlic and sauté for another minute until fragrant.
    4. Reduce the heat to low, and when the pasta is ready, add it to the skillet with the crispy pancetta and garlic. Toss everything together.
    5. Remove the skillet from heat and quickly pour in the egg and cheese mixture. Toss the pasta vigorously to coat it evenly, using some of the reserved pasta cooking water to create a creamy sauce.
    6. Serve immediately, garnished with additional Pecorino Romano cheese and black pepper if desired.
    Enjoy your delicious Spaghetti Carbonara!

    Slot Filling: Implement slot-filling prompts that require the model to populate predefined slots with relevant information. This approach can ensure structured and informative responses. Again, let’s have a look at an example of slot filling for a restaurant reservation chatbot:

    User Query: “I’d like to make a reservation for dinner tonight at 7 pm for two people at an Italian restaurant.”

    Slot-Filled Response:

    Bot: Sure, I can help you with that. Let me fill in the details for your reservation:

    – Restaurant Type: Italian
    – Reservation Date: [Date]
    – Reservation Time: 7:00 PM
    – Number of Guests: 2

    Please confirm the reservation details, and I’ll proceed with booking your table.

    In the example above, the chatbot identifies specific slots (such as restaurant type, reservation date, reservation time, and number of guests) from the user query and constructs a response that summarizes these details. The slots are filled with the relevant information extracted from the user’s request.

    The chatbot would then proceed to confirm the reservation details with the user, collect any missing information, and complete the reservation process based on the slot-filled details.

  9. Control Codes:
  10. 1. Token-Level Control Codes: Embed control codes or tokens within the prompt to instruct the model on how to generate responses. These tokens can indicate tone, style, or specific content requirements. Here’s an example of token-level control codes for instructing an AI model to generate text in different styles:

    User Prompt: "Generate a short story about a detective solving a mystery."
    Token-Level Control Codes:
    [STYLE: Detective Story]
    [GENRE: Mystery]
    [TONE: Suspenseful]
    [LENGTH: Short]
    Once upon a time in [SETTING: a gloomy, rain-soaked city], Detective [CHARACTER: John Smith] was on the trail of a puzzling case. [ACTION: He paced the dimly lit room], his mind racing as he tried to connect the [CLUE: cryptic notes] left at the crime scene.
    [STYLE: Detective Story]
    [GENRE: Mystery]
    [TONE: Suspenseful]
    [LENGTH: Short]
    As the hours ticked by, [CHARACTER: Detective Smith] meticulously pieced together the [CLUE: hidden messages] in the notes, uncovering a [PLOT TWIST: shocking revelation] that would change everything. The [SETTING: city's dark secrets] were about to be unveiled, and Detective Smith was determined to [ACTION: solve the mystery] once and for all.
    [STYLE: Detective Story]
    [GENRE: Mystery]
    [TONE: Suspenseful]
    [LENGTH: Short]
    The story unfolds with [CHARACTER: Detective Smith] using his [SKILL: keen deductive abilities] to solve the case, [ACTION: unraveling the web of deceit], and finally [OUTCOME: bringing the culprits to justice].
    [STYLE: Detective Story]
    [GENRE: Mystery]
    [TONE: Suspenseful]
    [LENGTH: Short]
    In the end, [CHARACTER: Detective Smith] stood triumphant, [ACTION: his determination and wit] prevailing over the forces of darkness. The city was safe once more, thanks to the fearless detective and his [CHARACTER TRAIT: unwavering resolve].

    In the example above, token-level control codes are used within square brackets to indicate various aspects of the text generation, including the style of the story (detective), the genre (mystery), the tone (suspenseful), the desired length (short), specific story elements (character, setting, clue), and even the plot twist. These control codes provide fine-grained guidance to the AI model, allowing it to generate a text that aligns with the specified attributes and style.

    2. Positional Control: Use control codes at specific positions in the prompt to influence the model’s behavior. For instance, placing a control code at the beginning of the prompt can set the tone or style of the response. Here’s an example of positional control codes for generating a persuasive essay with different sections:

    User Prompt: "Write a persuasive essay about the importance of renewable energy sources."
    Positional Control Codes:
    Renewable Energy: A Sustainable Future
    In today's rapidly changing world, the importance of renewable energy sources cannot be overstated. [TONE: Positive] [STYLE: Persuasive]
    Environmental Benefits
    One of the primary reasons why renewable energy sources are crucial is their positive impact on the environment. [TONE: Informative]
    Economic Advantages
    Moreover, renewable energy offers substantial economic advantages that cannot be ignored. [TONE: Informative]
    Energy Security
    In addition to environmental and economic benefits, renewable energy sources enhance energy security. [TONE: Informative]
    In conclusion, the transition to renewable energy is not just an option; it is a necessity. [TONE: Persuasive]
    A Sustainable Future Awaits
    In summary, the adoption of renewable energy sources is the key to a sustainable future for our planet. It is time for us to embrace the change and work towards a cleaner, greener, and more prosperous world. [TONE: Positive]

    In the example above, positional control codes are placed within brackets at specific positions within the essay to guide the model’s behavior:

    - `[INTRODUCTION]` sets the stage for the introduction of the essay, specifying the essay's tone as positive and style as persuasive.
    - `[PARAGRAPH 1]`, `[PARAGRAPH 2]`, and `[PARAGRAPH 3]` guide the model to provide informative content in paragraphs 1, 2, and 3.
    - `[PARAGRAPH 4]` instructs the model to conclude the essay with a persuasive tone.
    - `[CONCLUSION]` marks the beginning of the conclusion section, specifying a positive tone.

    By using positional control codes, you can control the style, tone, and content of different sections of the essay, resulting in a well-structured and persuasive piece of writing.

  11. Repetition and Revision:
  12. Iterative Prompts: If the initial model response is not satisfactory, use an iterative approach by rephrasing or modifying the prompt based on the model’s output. This allows you to guide the model towards the desired response through multiple interactions.

  13. Contextual Prompts:
  14. Context Inclusion: Provide context from previous interactions or user history to create more personalized and context-aware prompts. This can help the model generate responses that align with the ongoing conversation.

  15. Scenarios and User Stories:
  16. Narrative Prompts: Frame prompts as user stories or scenarios to encourage the AI model to provide responses within a specific narrative context. This can be valuable for generating storytelling or conversational content.

    In conclusion

    The evolution of AI prompting, from traditional hard prompts to the more flexible and nuanced soft prompts, represents a significant leap forward in human-machine interactions. As we’ve explored the fundamental differences between these two approaches, it becomes evident that soft prompting offers a wealth of benefits, including enhanced flexibility, adaptability, creativity, and improved interpretability in AI systems.

    The choice between hard and soft prompting ultimately depends on the specific requirements of each AI application. Hard prompts are ideal when precise, unambiguous instructions are necessary, and predictability is paramount. On the other hand, soft prompts shine in scenarios where natural, context-aware interactions, creative responses, and bias mitigation are key.

    To effectively implement soft prompts, we’ve delved into various techniques, including prompt engineering, template-based prompts, slot filling, control codes, and more. These strategies provide the means to guide AI models to generate desired responses while maintaining the necessary degree of user control.

    In conclusion, soft prompting represents a pivotal shift in the AI landscape, enabling more human-like interactions and responses from machines. As AI systems continue to advance, the art of soft prompting will play a central role in shaping the future of AI applications, making them more adaptable, creative, and aligned with human intent. Whether it’s crafting persuasive essays, generating recipes, or assisting with complex tasks, the journey from hard to soft prompting offers an exciting frontier in AI research and development.