Prompt Engineer — LLM Fundamentals
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What you'll learn:
What are They?** - **Description:** Begin with the fundamentals. Understand what LLMs are, how they work at a high level (don't dive into complex technical details yet), and their capabilities. Learn about different types of LLMs (e.g., GPT, BERT) and their common applications. This will include understanding the difference between generative and discriminative models at a basic level. Also, learn about the history of LLMs. - **Resources/Activities:** - Read introductory articles or blog posts on LLMs (search for "What are LLMs?" or "LLM explained simply"). - Watch a short, animated explainer video on how LLMs work. - Explore a user-friendly LLM like ChatGPT (if available) to get a feel for interacting with one. Try asking basic questions and prompts. - **Expected Outcomes:** - Understand the basic definition and purpose of LLMs. - Recognize common applications of LLMs. - Have a basic understanding of LLM types. - Familiarity with interacting with a simple LLM.
Personal Notes:
What you'll learn:
The Simplified View & Tokenization** - **Description:** Explore a simplified overview of the architecture of an LLM (focus on the input, processing, and output stages; avoid deep technical specifics like attention mechanisms for now). Then, delve into the concept of tokenization - how text is broken down into numerical representations for the model to understand. - **Resources/Activities:** - Read articles or watch videos explaining LLM architecture in a beginner-friendly manner (search for "LLM architecture simplified"). - Experiment with a tokenizer tool (many are available online). Input different sentences and see how they're tokenized. Observe the token counts and patterns. - **Expected Outcomes:** - Basic understanding of the core components of an LLM. - Understand the concept of tokenization and its importance. - Be able to visualize text broken down into tokens.
Personal Notes:
What you'll learn:
The Basics** - **Description:** Introduce the core concept of prompt engineering. Learn what prompts are and why they are important. Start with simple prompt techniques: clear instructions, specificity, and directness. Practice forming effective questions and commands. - **Resources/Activities:** - Read a few introductory articles on prompt engineering (search for "Prompt engineering tutorial for beginners"). - Experiment with the LLM you used on Day 1. Test different prompts, starting with simple questions and then gradually increasing complexity, following the principles of clarity and specificity. - Try different prompt formats (e.g., question-answer, instruction-response). - **Expected Outcomes:** - Define prompt engineering and understand its purpose. - Know the fundamental principles of creating effective prompts. - Practice writing basic prompts for an LLM. - Realize the importance of prompt design on output quality.
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What you'll learn:
Constraints and Examples** - **Description:** Learn about prompting techniques that help you get better results. Learn about setting constraints (e.g., character limits, tone requirements). Introduce the use of "few-shot learning" - providing examples to guide the LLM. - **Resources/Activities:** - Explore resources on prompt engineering best practices, including examples. - Practice using constraints in your prompts. For example, ask the LLM to summarize a topic in a specific number of words or in a particular tone. - Experiment with few-shot prompting by providing examples of the desired output format. - **Expected Outcomes:** - Understand how to use constraints to control LLM output. - Learn how to leverage examples in prompts for better guidance (few-shot learning). - Practice creating prompts that employ these techniques.
Personal Notes:
What you'll learn:
Different Task Types** - **Description:** Practice prompt engineering for various tasks, such as: - Text summarization. - Translation. - Question answering. - Content creation (e.g., poems, short stories). - Code generation (if applicable). - **Resources/Activities:** - Find online examples of prompts for different tasks. - Spend time practicing prompt engineering for the tasks listed above. - Evaluate the results you obtain with different prompts and techniques. - **Expected Outcomes:** - Gain experience writing prompts for a variety of tasks. - Learn how to tailor prompts to specific tasks. - Develop an understanding of how prompt design impacts the output's quality and relevance.
Personal Notes:
What you'll learn:
Iterative Refinement & Role-Playing** - **Description:** Focus on more advanced techniques: Iterative Prompting (improving prompts based on feedback) and role-playing. Role-playing involves asking the LLM to take on a persona (e.g., "Act as a helpful assistant"). - **Resources/Activities:** - Read about iterative prompt refinement strategies (e.g., using the LLM's output to generate better prompts). - Practice role-playing. Experiment with different personas. - Iterate on your prompts and refine them based on the LLM’s responses. - **Expected Outcomes:** - Understand the process of iterative prompt refinement. - Practice role-playing for different use cases. - Learn how to use the LLM's output to improve prompts.
Personal Notes:
What you'll learn:
- **Description:** Discuss ethical considerations of LLMs (bias, misinformation, potential misuse). Learn about future trends in the field. Discuss how prompt engineering is evolving. - **Resources/Activities:** - Read articles about the ethical implications of LLMs. - Explore discussions on the future of LLMs and prompt engineering. - Consider potential applications of prompt engineering in your own interests/field. - **Expected Outcomes:** - Understand the importance of ethical considerations when working with LLMs. - Gain awareness of future trends in prompt engineering. - Think critically about how prompt engineering can be applied.
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