Leveraging Effective Prompt Engineering for Your Business
Business owners often lack the time to explore every technical detail about AI models to integrate them into their operations. When discussing AI in business applications, it’s crucial to highlight how it can save time or money. A good example is the Klarna AI Assistant, which manages two-thirds of customer service chats, equivalent work of 700 full-time agents.
The secret sauce that will catapult your business productivity to astronomical levels is fine-tuning and prompt engineering. In this article I will explore prompt engineering for businesses: its importance and how you can utilize it to tailor any available AI model specifically for your business needs.
Prompt engineering is a relatively new concept that has evolved into a distinct discipline. Its objective is to effectively guide the language model to produce the desired output through a set of user-provided instructions. While Large Language Models (LLMs) are effective at answering questions with minimal context, to fully leverage their capabilities, it’s important to supply as much detail and information as possible to guide their responses. Fortunately, there are established techniques that engineers employ, which you can also apply in your business to maximize this technology.
Let’s analyze Klarna AI assistant, which resolves customer inquiries based on their data. To implement a similar system in your business, you would need to train the model with your business data and then direct the model to respond appropriately. You can adopt this simple structure to refine the AI’s responses:
{Context}
{Examples of desired responses}
{Examples of undesired responses}
In the context section, the engineer explains the model how it should interpret the training data. This data might include PDF or CSV files, but the context provides the model with the necessary perspective.
A basic prompt might not yield impressive results, but a well-constructed prompt can lead to more accurate outcomes:
Basic prompt:
Well structured prompt:
By offering detailed context and employing role-play mechanisms to guide the AI’s behavior, you ensure more precise responses. Another factor to consider is the response length. You wouldn’t want the AI to write an essay every time a customer inquires about their account status, so you could limit the model to respond with no more than 100 characters.
When instructing how the model should answer certain queries, you can anticipate the various ways customers might interact with your AI. Following this framework helps train the model to generate responses similar to your examples.
Following these examples will help your models to respond accordingly to your context. Thanks for reading.