Get an overview of prompt engineering, its usage, and how it assists with AutoQA scoring.
AI-based scoring is the bedrock of PeopleQX’s easy-to-understand, easy-to-use AutoQA scoring system, and it’s all driven by the foundation of effective prompt engineering. So, it’s natural to ask the fundamental question of all: what is prompt engineering?
At its core, prompt engineering is known as the practice of designing and refining prompts to guide generative AI. These prompts can be in the form of either instructions or questions, and contain different elements depending on the desired result.
In general, prompt engineering is used to guide AI models when it comes to delivering the desired results through refining large language models (LLMs).
One of the most obvious examples is through how we produce content in generators like ChatGPT; other examples include AI-powered chatbots, platform recommendations, data insights used by companies, and question-answering for help centers.
When it comes to PeopleQX, though, there is a difference in how AI prompting is used. Through AutoQA, the AI is prompted via a series of questions to evaluate agent conversations based on an established scorecard that’s tailored to your brand’s specific needs.
To validate AI scoring and ensure accuracy, AI validation is conducted. This will involve testing your AI scoring with existing audio files based on benchmark scores – which are evaluations manually done by humans – when it comes to answering questions.
How the PeopleQX AI model does this is through classifying comparison results made between its AutoQA process and benchmark scores, otherwise known as the ground truths determined by human analysts.
This classification model utilizes a combination of precision and recall, known as the F1 score, to be grouped into four predefined labels: True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). You can learn more about what these labels mean and their importance here.
Through this, the questions used for AI scoring are progressively refined to accurately assess agent performance based on scorecards. By relying on such processes, prompt engineering plays a pivotal role to better the connection between humans and AI, as well as in improving machine learning efficiency and accuracy.
Keep in mind: like other AI models, PeopleQX’s AI scoring system functions based on prompts – the questions in your scorecard. It’s therefore vital to ensure to write in a way that is clear and easy to understand so that the AI can evaluate better. You can find out the best practices when it comes to building your scorecard here.