As you’re here, it’s quite likely that you’re already well-informed about the wonders of Generative AI possibly through tools like ChatGPT, DALL-E or Azure OpenAI. If you’ve been surprised by the capabilities of this technology and wondered, “Hey, this tech is awesome, but how do I boost the response quality?”—you’re in the right place.
In my point of view, there are two methods to enhance the quality of coming from Generative AI: prompt engineering and fine-tuning. Understanding how they can help us to unlock the full potential of our AI-powered applications. Also, they allow us to leverage the knowledge available in pre-trained models like GPT-3 (i.e., DaVinci, Curie, Babbage) while specializing these models for our business scenario’s unique requirements. This efficiency is especially crucial in fast-paced industries where time-to-market is critical.
What Is Prompt Engineering?
Prompt engineering is all about effectively communicating our needs to AI applications, ensuring they can assist us in a precise and accurate manner. Let’s take a moment to delve deeper into this concept.
Imagine using Bing Chat AI to solve a math problem. If we ask a vague question like, “Can you help me with a math problem?” we’ll likely receive a response, but it might not be exactly what we need. However, if we provide clear and specific instructions like, “Please calculate the sum of 13 and 7,” we’re practicing prompt engineering. The result? We get a more accurate and relevant answer because we’ve guided the AI with a well-structured instruction or prompt.
On the business front, think about requesting market or sales trends analysis from an OpenAI-based tool. If we ask a broad question like, “Tell me about the banking industry,” we might not receive the detailed insights we require. However, by applying prompt engineering and saying, “Please provide a detailed report on the current market trends for the US banking industry, including data on our top competitors’ recent strategies and consumer preferences,” we’ve set clear expectations. This precision allows the selected Large Language Model (LLM) to understand our request comprehensively and deliver valuable insights.
So, whether we’re solving math problems or seeking in-depth market analysis, remember that the clarity of our instructions matters. The better we engineer our prompts, the more precise and valuable the responses we receive will be.
I do recommend fostering a culture of prompt engineering and encouraging teams to share best practices and learnings in crafting effective prompts. This collaborative approach can significantly enhance effectiveness, boost productivity and lead to smarter decision-making. It’s an investment that pays off in more ways than one!
It’s becoming increasingly common to observe developers prioritizing the acquisition of prompt engineering skills and incorporating prompt optimization tasks into the application testing phase.
What Is Fine-Tuning?
The second method to unlock the full potential of Generative AI is fine-tuning. It empowers individuals to take benefit of pre-trained AI models and tailor them to specific domains, making it an invaluable skill for those seeking to harness AI effectively and align it with their unique requirements.
In essence, fine-tuning allows us to refine pre-trained models such as GPT-3.5 or GPT-4 for precise applications by subjecting them to a more targeted dataset that closely aligns with the specific task at hand (i.e., an application focused on giving legal opinions based on existing knowledge base).
Fine-tuning involves furnishing the model with a dataset containing labeled examples pertinent to the specific task at hand. My learning is that successful fine-tuning lies in the additional data and training it entails. This includes providing feedback or rating the AI’s responses and steering the system with meticulously designed questions using prompt engineering techniques.
While it’s true that fine-tuning in Generative AI comes with additional costs, it’s often three times more cost-effective in the long run compared to training a model from scratch or investing significant hours in optimizing queries using prompt engineering techniques, especially when dealing with complex tasks like building a chatbot in a domain that requires high knowledge.
Why Invest In Fine-Tuning?
As previously mentioned, fine-tuning and prompt engineering represent distinct methods for harnessing LLM capabilities to tackle specific tasks. Certain tasks may require fine-tuning to achieve optimal results, while others can be effectively addressed solely through prompt engineering. This typically brings the questions: Why and when should we consider investing in fine-tuning?
Both of the mentioned methods possess their respective advantages and drawbacks; it’s really dependent on the particular use case and available resources. Based on the insights captured from projects my team has been involved in, here are some key takeaways:
• Relying solely on prompt engineering can be challenging when striving for high accuracy and quality, as LLMs may still generate irrelevant or erroneous outputs. Fine-tuning becomes invaluable in enhancing the accuracy and quality of the model’s outputs. It also mitigates the risk of generating irrelevant or contradictory responses which translates into customer satisfaction.
• Crafting effective prompts through prompt engineering can be a time-consuming and laborious process, often necessitating extensive trial and error and experimentation to discover the optimal prompt. Moreover, extended prompts may introduce latency and diminish model efficiency.
• Fine-tuning reduces dependence on prompt design, enhancing model robustness and consistency across different input formats and styles. It also expands the model’s capabilities, enabling it to perform tasks beyond those supported by the original pre-trained model.
For these reasons, I do recommend investing in fine-tuning in any organization looking to harness the power of AI effectively in use cases targeting to stay competitive. For sure, fine-tuning reduces the high costs associated with training models from scratch, making AI adoption more financially feasible.
In conclusion, fine-tuning within the realm of Generative AI stands as a transformative force for both businesses and developers. It facilitates the effective utilization of advanced pre-trained AI models across a diverse spectrum of applications. Its efficiency, cost-effectiveness and capacity to harness pre-existing knowledge render it an indispensable asset within the contemporary AI landscape. To stay competitive and drive innovation, investing in fine-tuning isn’t just a choice but a necessity.