Learn Prompt Engineering for Interview, Written Test, and Certification through Practice Tests (MCQs) : For all Levels
What you will learn
Learn basics of Prompt Engineering to craft concise, effective prompts for AI models like GPT-4, improving accuracy and relevance across diverse applications.
Apply advanced techniques like Chain-of-Thought, In-context, Dynamic, Zero-Shot, and Few-Shot prompting to break down complex tasks and enhance model outputs.
Grasp the principles of prompt design, Optimize responses, Craft precise prompts to maximize AI model accuracy, and relevance across various use cases.
Automate prompt testing and optimization with advanced AI tools, Implement automation strategies for prompt testing and refinement using AI tools and platforms
Design and implement practical prompt engineering strategies, optimize prompt systems for real-world applications in business, creativity, and research.
Learn how to use prompts in specific domain such as NLP, Customer support, Research, Healthcare, HR, Project Management, Programming, Content Creation, Finance
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- The most accurate and helpful response to a user asking for medical advice on treatment of a cold would be an option that provides both a general overview of cold symptoms and management options, including over-the-counter medications and home remedies (B), as well as a specific recommendation for what they should take, considering their personal context or any reported allergies or sensitivities (D).
- Option A (“Translate: ‘Hola’ β ‘Hello.’ Now, translate: ‘Gracias.’)” is an effective prompt using In-Context Learning because it provides a clear example of the task in the context of the model’s previous instructions. It allows the model to understand the pattern and apply it to the new task (translating “Gracias”). Options B (“Write a sentence in Spanish.”), C (“Translate: ‘Goodbye’ β ‘AdiΓ³s.'”), and D (“Describe the translation process.”) do not provide an in-context example for the AI to follow.
- For students practicing prompt engineering, creating a chatbot to generate responses based on user questions using varied prompts (A) is an ideal hands-on project because it directly involves the student in interacting with and understanding how different prompts can influence the AI’s outputs. Options B (Developing an e-commerce platform using JavaScript and Node.js.), C (Building a machine learning pipeline for image classification.), and D (Writing a thesis on quantum computing advancements.) are valuable skills but do not specifically relate to prompt engineering.
- To automate the process of testing multiple prompts for an AI-driven customer service tool, you would:
- Create a script to test prompts in bulk (A).
- Monitor the success rate of prompt outputs (C) to evaluate how well each prompt performs.
- Adjust the script to optimize poor-performing prompts (D) as part of an iterative process to refine and improve the responses.
Manually testing each prompt one by one (B) is not efficient and defeats the purpose of automation.
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