Prompt Engineering Test to evaluate candidates' prompt engineering skills

A Prompt Engineering Test measures a candidate's proficiency in prompt engineering and in designing development programs, it involves writing effective prompts that elicit the desired responses from Generative AI systems. The test evaluates a candidate's ability to create prompts that yield accurate and relevant outputs and their understanding of optimizing and fine-tuning the prompts to achieve specific goals. It may involve tasks such as generating prompts for different queries, evaluating the quality of prompts, and adjusting prompts to improve the output of AI models.

What is prompt engineering?

Prompt engineering involves crafting and optimizing questions or instructions to elicit specific, helpful responses from Generative AI models. It's a strategic process that translates business needs and human intentions into actionable responses from Generative AI models. It ensures that the system aligns closely with desired outcomes.

What is inside this Prompt Engineering Test?

This test consists of twenty multiple-choice questions across proficiency levels to assess candidates’ prompt engineering skills. The duration of this assessment is thirty minutes.

What skills does this Prompt Engineering Test cover?

The test covers the following skills- familiarity with generative AI tools, prompt building skills, evaluating AI output, privacy & confidentiality, data security/information security, and responsible AI – knowledge.

What roles can you assess using the Prompt Engineering Test?

The test can be used for any function where GenAI can be used to support work deliveries. Such as roles across functions in an organization. For example, it can be used in marketing for content management.

Questions and answers related to prompt engineering

1. What is a recommendation for writing clear and concise prompts?  

The ideal recommendation for writing clear and concise prompts is to keep the instructions straightforward and specific. Using simple language, avoiding ambiguity, and ensuring that the prompt directly addresses the task at hand is essential. Additionally, breaking complex instructions into smaller, manageable parts can help improve understanding and accuracy.

2. What is a Generative AI model?  

A generative artificial intelligence model is an artificial intelligence algorithm that can generate new data or content that closely resembles the existing data it was trained on. This means that given a dataset, a generative model can learn and create new samples with similar characteristics to the original data.

3. What are language models?  

Language modelling (LM) is artificial intelligence that helps computers understand and interpret human language. They use statistical techniques to analyze large amounts of text data, learn patterns and relationships between words, and then generate new sentences or even entire documents based on this knowledge.

4. Name some applications of Generative AI models.  

Generative AI models have various applications in various fields, namely computer vision, natural language processing, and healthcare. In computer vision, generative models are used for image generation, style transfer, and data augmentation. In natural language processing, they can be used for text generation, language translation, and chatbot development.