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Prompt engineering vs. fine-tuning: What's the difference?
Prompt engineering and fine-tuning are both practices used to optimize AI output. But the two use different techniques and have distinct roles in model training.
AI and machine learning applications have proliferated over the last decade. Recently, generative AI models such as ChatGPT have brought AI into the mainstream for many users and businesses that might never have previously considered these technologies.
Despite their promise, however, AI and its underlying ML models are not "intelligent." In reality, the term AI refers to little more than systems that can find relationships within large and complex data sets and then organize and express those relationships to users in some meaningful way.
Consequently, AI systems are heavily dependent on optimization techniques. Model optimizations serve two fundamental purposes: driving more desirable, meaningful results and improving performance. Two key optimization techniques used today are prompt engineering and fine-tuning.
What is prompt engineering?
One of the oldest axioms in computer science is "garbage in, garbage out" -- a phrase that's every bit as true in today's AI age as it was when programming the earliest mainframes.
AI systems are neither psychic nor telepathic. Because they cannot intuit or recognize the user's ultimate intentions, they are completely dependent on user input. Questions or requests that a user poses to an AI system, such as ChatGPT, are referred to as prompts.
When a user inputs a prompt, the AI parses -- breaks down or disassembles -- the main elements of the user's prompt. The system then uses those elements within its internal logic and models to process data, yield results or take actions.
Consequently, the quality and completeness of AI output are deeply affected by the quality and precision of the user's prompt. In short, an AI can produce more meaningful and specific answers if a user asks more meaningful and specific questions.
Prompt engineering is a specialized AI skill or role that involves guiding and shaping ML model responses by relying on the user, or prompt engineer, to craft more carefully phrased and specific queries. Understanding how to craft optimal prompts often requires a deep understanding of the ML model, including its algorithmic architecture and the constraints of the data sets available.
The prompt optimization process often involves asking increasingly specific and detailed questions or testing out different ways to phrase instructions. The goal is to use prompts to elicit better behaviors or outputs from the AI. Prompt engineers typically experiment with various types of instructions and formats to find the most desirable and relevant AI responses.
What is fine-tuning?
An AI system can also be optimized for performance through a training technique called fine-tuning.
All AI is not created equal, and most models are specialized for specific purposes, such as customer support or medical diagnoses. Fine-tuning lets a business optimize an existing model's performance or otherwise shape its behavior by applying new -- often custom -- data sets to train its weights.
When implemented correctly, fine-tuning can adapt an AI system to specific tasks or knowledge domains, often improving performance by producing faster and more relevant results. For example, fine-tuning can be useful for text classification, chatbots and other interactive conversational systems, and sentiment analysis.
The key to effective fine-tuning is the additional data and training. Additional data sets give the AI new raw information, while training helps the AI contextualize that data and understand how to correlate questions to the most relevant and appropriate answers. Training is often coupled with a feedback or rating system that scores the AI's responses and guides the system to better results.
Prompt engineering vs. fine-tuning
Prompt engineering and fine-tuning are both important means of optimizing AI performance and output. However, there are several key differences between the two techniques:
- Prompt engineering focuses on eliciting better output for users, whereas fine-tuning focuses on enhancing model performance on certain tasks.
- Prompt engineering aims to improve output by creating more detailed and effective inputs, whereas fine-tuning involves training a model on new data to improve knowledge in specific areas.
- Prompt engineering offers more precise control over an AI system's actions and outputs, whereas fine-tuning can add detail and depth to relevant topic areas.
- Prompt engineering demands almost no computing resources, as prompts are created by people. In contrast, the additional training and data used for fine-tuning can demand significant computing resources.
Using both prompt engineering and fine-tuning techniques can improve model behavior and enhance outputs. However, the effectiveness of both techniques depends heavily on the knowledge and skills of human engineers, typically requiring AI professionals with suitable skills in prompt engineering as well as ML training.
What are prompt tuning and plugins?
Several additional concepts relate to extending and optimizing the behavior of an AI platform, namely prompt tuning and plugins.
Prompt tuning
Prompt tuning is a variation on AI optimization. Whereas fine-tuning is intended to train a model for specific tasks and prompt engineering aims to elicit better AI responses from the front end, prompt tuning takes a combined approach.
Prompt tuning takes the most effective prompts or cues and feeds them to the AI model as task-specific context. For example, a common question or phrase -- for example, "What is my account balance?" -- could be taught to the AI to help optimize the model and guide the system toward the most desirable decision or action.
Prompt tuning isn't about asking a better question or making a more specific request. It's simply a means of identifying more frequent or important questions and training the AI to respond to those common prompts more effectively. The benefit of prompt tuning is that it can be used to modestly train models without adding any more data, resulting in considerable time and cost savings.
Plugins
Plugins, in this context, are extensions that enable an AI model to access tools or data outside of the model. A plugin might enable a model to perform an internet search, access a corporate database or execute some piece of specialized code in response to an appropriate user prompt.
For example, ChatGPT already supports a range of plugins created by major service providers, including Expedia, Kayak and Slack. OpenAI also offers an in-house web browser, a code interpreter and knowledge base retrieval plugins for the model.
