Prompt Engineer — Business & Use Cases
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What you'll learn:
- Description: Start with a gentle introduction to Artificial Intelligence (AI), focusing on Large Language Models (LLMs). Understand what LLMs are, how they work at a basic level (without diving into complex technical details), and why they are relevant. Define prompt engineering and its importance. - Specific resources or activities: - Read: "AI for Beginners" (various online resources available like articles from IBM or Google). Focus on the sections about NLP and LLMs. - Watch: Short explainer videos on YouTube about LLMs and AI basics (search for "AI Explained for Beginners" or "What is an LLM?"). - Activity: Define 'prompt engineering' in your own words. List 3 potential applications of AI in your daily life. - Expected outcomes: Understand the basic concepts of AI, LLMs, and prompt engineering. Have a foundational understanding of what prompt engineering is.
Personal Notes:
What you'll learn:
Basic Techniques - Description: Learn the core components of a good prompt. Explore different prompt types (e.g., task-oriented, role-playing, creative). Introduce basic prompt techniques: clear instructions, context setting, specifying desired output format. - Specific resources or activities: - Read: "The Prompt Engineering Guide" or similar online tutorials. Focus on sections about prompt structure. - Practice: Use an LLM (like ChatGPT, Bard, or others) and experiment with different prompt structures. Start with simple tasks like summarizing text or generating short poems. Experiment with varying levels of detail in your prompts. - Activity: Write 3 different prompts for the same task (e.g., "Write a short story about a cat"). Vary the prompt techniques for each (e.g., adding context, specifying output format). - Expected outcomes: Understand the components of a good prompt and practice basic prompt techniques. Demonstrate an ability to create different prompt structures.
Personal Notes:
What you'll learn:
- Description: Explore how prompt engineering can be used in marketing and content creation. Learn about prompts for generating marketing copy, brainstorming ideas, and writing different content formats. Consider examples: social media posts, blog introductions, ad copy. - Specific resources or activities: - Read: Case studies on using LLMs in marketing (search for "AI in marketing use cases"). - Practice: Use an LLM to generate content for a made-up business. Experiment with prompts that generate different content types. e.g., "Write a social media post advertising a new coffee shop". "Create 3 different headlines for a blog post about the benefits of meditation." - Activity: Create a basic marketing plan for a fictional product using an LLM to brainstorm ideas and generate initial content. - Expected outcomes: Understand how prompt engineering can be applied to marketing and content creation. Create different content types using prompt engineering techniques.
Personal Notes:
What you'll learn:
- Description: Learn about prompt engineering in customer service and automation. Explore prompts for creating chatbots, answering FAQs, and generating customer support responses. - Specific resources or activities: - Read: Articles about AI-powered customer service and chatbots. - Practice: Use an LLM to simulate a customer service interaction. Create prompts that handle common customer inquiries. - Activity: Create prompts for handling a common customer service issue (e.g., "What is your return policy?") and for escalating the issue if necessary. - Expected outcomes: Understand how prompt engineering can be applied to customer service and automation. Demonstrate an ability to create prompts for common customer service scenarios.
Personal Notes:
What you'll learn:
- Description: Explore advanced prompt engineering techniques such as few-shot learning, chain-of-thought prompting, and prompt chaining. Understand how to refine prompts through iteration and feedback. - Specific resources or activities: - Read: Articles on advanced prompt engineering techniques (search for "advanced prompt engineering techniques"). - Practice: Experiment with few-shot learning and chain-of-thought prompting using an LLM. Refine your prompts based on the output. - Activity: Choose a complex task and create a prompt using a few-shot learning or chain-of-thought approach. Iterate on the prompt to improve the result. - Expected outcomes: Understand and practice advanced prompting techniques. Demonstrate the ability to refine prompts through iteration.
Personal Notes:
What you'll learn:
- Description: Explore how prompt engineering can be used for research, data analysis and information retrieval. Learn prompts for summarizing research papers, extracting specific data from documents, and analyzing data. - Specific resources or activities: - Read: Articles/tutorials on LLMs for research or data analysis. - Practice: Input various reports or articles into an LLM and use prompts to extract key information, summarize the content, or generate insights. - Activity: Identify a research question. Use an LLM to find relevant research papers, summarize them, and analyze the findings based on your prompts. - Expected outcomes: Understand how LLMs can be used for research and analysis. Apply prompt engineering to extract and summarize data from documents.
Personal Notes:
What you'll learn:
- Description: Apply your knowledge to a more complex project. Consider a real-world business scenario or personal project. Reflect on your learning journey and identify future directions for your prompt engineering skills. - Specific resources or activities: - Project: Choose a small business or project and use LLMs to address a specific business need (e.g., generate marketing content, automate customer service, analyze market trends). - Activity: Create a presentation or report summarizing your project, the prompts you used, the results you achieved, and your conclusions. Reflect on what you learned and areas for further development. - Expected outcomes: Successfully complete a project utilizing prompt engineering. Understand the entire process from start to finish and identify areas for continuous learning.
Personal Notes:
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