You likely know what a prompt is and why it’s important to develop good ones. AI is a far cry from a person but all things do better with clear communication and AI is no different.
Prompt tuning is fine-tuning your prompt to achieve faster preferable results. The better your prompt, the more efficient and productive you will be with AI.
Large language models (LLMs) like ChatGPT, are trained on vast amounts of internet data. Now, notice the word internet is highlighted – it’s important to keep that in mind while you work with most LLMs. All that knowledge poured into that AI was pulled from the internet. Facebook, Wikipedia, Reddit, random blogs and pages and the list goes on.
How many times have you identified false information online? Never assume AI has all the right answers!
These models are incredibly versatile, capable of analyzing legal documents or composing comical poetry about a soccer team. In fact, LLMs can do almost anything – but as you likely learned in school. If you’re a Jack-Of-All-Trades then you’re a master of none. Most AI has too much generalized information so it’s up to you to fine-tune it for specific tasks (unless you use AkzisAI as we have that built-in).
To enhance these models for specific tasks requires lots of data and extensive retraining. Or you can take a shortcut; prompt tuning is a simpler, more energy-efficient alternative.
Going From Fine Tuning to Prompt Tuning
Fine tuning has been the standard method to customize pre-trained LLMs for tasks. This process involves gathering thousands of examples and adjusting the model accordingly. Basically, you overload the AI with highly specific information forcing it to focus-up on your needs. It’s like teaching an old dog new tricks by showing it many, many examples. But prompt tuning changes the game. It allows companies with limited data and resources to tailor massive models to narrow tasks without the heavy data requirements.
How Does Prompt Tuning Work?
Prompt tuning uses carefully crafted prompts to give AI models task-specific context, improving their performance on those tasks. These prompts could be additional words provided by humans or AI-generated numbers incorporated into the model’s embedding layer. This method guides the model towards making more accurate predictions or decisions related to the task at hand.
What's A Hard Prompt?
What’s A Hard Prompt?
A hard prompt is a human-engineered prompt. It’s rigid and direct because it’s designed to elicit a specific type of response from the AI model.
Here’s an example of a hard prompt for a task where a large language model (LLM) is used to provide legal advice regarding copyright law:
Hard Prompt Example for Legal Advice:
- Prompt: “Provide a summary of copyright infringement penalties under U.S. law for an individual caught distributing copyrighted materials without permission.”
- Model’s Context: This prompt is explicitly crafted to guide the AI towards providing specific legal information. It contains clear directives on the task (provide a summary), the topic (copyright infringement penalties), the jurisdiction (U.S. law), and the scenario (individual distributing copyrighted materials).
This type of prompt is hardcoded by a human to ensure that the AI focuses precisely on the required legal aspect, minimizing the risk of irrelevant or overly broad responses. Without this specific type of prompting the AI model will return a lot of irrelevant information that has nothing to do with the request.
Soft Prompts Work Even Better
While human-engineered prompts (hard prompts) have been effective, AI-designed soft prompts (used in prompt tuning) are proving to be even better. These soft prompts are not understandable to humans (unless you’re an expert-level coder) and are a series of embeddings or numerical strings that fine-tune the model’s responses. They’re more nuanced and can better distill complex knowledge from the model. Basically, we’re using enhancing AI’s performance without extensive retraining.
Since soft prompts are composed of numerical values that directly interact with the model’s architecture, I can’t easily offer you an example because they’re not interpretable in the conventional textual sense.
Instead, I’m going to illustrate the concept of a soft prompt with a simplified representation just to give you an idea of how they can function.
Conceptual Example of a Soft Prompt
In this scenario, a large language model is tasked with generating a market analysis report focused on the technology sector. Instead of crafting a textual prompt, a soft prompt would be generated by the AI system. This would adjust the model’s internal state to tune its outputs toward technology market insights.
Soft Prompt Representation:
- Initial Input: Numerical embeddings adjusted to emphasize technology terms, market trends, and analysis tone.
- AI Model’s Adjusted Parameters:
- Vector for “technology”: [0.12, -0.08, 0.33, …]
- Vector for “market analysis”: [0.47, 0.21, -0.14, …]
- Tone adjustment for analytical rigor: [0.05, 0.02, 0.03, …]
In practice, these vectors (soft prompts) are inserted into the model’s processing pathway, influencing how it interprets and generates content without the need for explicit textual guidance. The AI uses these vectors to prime itself to produce outputs aligned with the desired focus on technology market analysis.
In real-world applications, these vectors are complex and tailored by the AI through a process of machine-learning and optimization. This guarantees that the output is highly specific and relevant to the task without direct human intervention in crafting the prompt text. These embeddings are often incomprehensible to humans and are chosen because they statistically optimize the AI’s performance on training examples like the target task.
Applications and Benefits of Prompt Tuning
Prompt tuning is particularly valuable in multitask learning and continual learning:
- Multitask Learning: Prompt tuning enables models to switch between tasks quickly, using universal prompts that are easily adaptable to various tasks.
- Continual Learning: It allows AI models to learn new tasks without forgetting previously learned information, maintaining their versatility over time.
Prompt tuning is making it faster and easier to specialize AI models for specific tasks, surpassing traditional methods like fine tuning and prompt engineering in both efficiency and effectiveness. As AI continues to evolve, the role of prompt engineers may diminish, but the impact of their innovations will shape the future of AI interactions.
