12 Kaggle Notebook to Master Prompt Engineering
Master Prompt Engineering with These 12 Must-See Kaggle Notebooks
Mastering prompt engineering is crucial for unlocking the full potential of instruction-tuned large language models (LLMs). Prompt engineering is the key to guiding LLMs to perform specific tasks, generate accurate responses, and improve their overall utility in various real-world applications.
This article provides a comprehensive guide to mastering prompt engineering through 12 curated Kaggle notebooks, each offering practical insights and hands-on exercises.
From foundational best practices to advanced topics like iterative prompt development, text summarization, textual inference, and sentiment analysis, this resource is designed to equip learners with the skills needed to create, test, and refine prompts for instruction-tuned LLMs.
Additionally, the article delves into specialized areas such as chatbot development, customer service automation, and LLM output validation, ensuring readers are well-prepared to build and optimize AI-driven systems.
This guide is essential for AI practitioners, data scientists, and developers who are eager to enhance their understanding of prompt engineering and apply it effectively to real-world LLM applications.
Whether you’re a beginner looking to explore the basics or an experienced professional seeking to sharpen your skills, these notebooks offer valuable knowledge and practical experience in shaping AI behavior through prompt design.
Table of Contents:
Prompt Engineering Best Practices for Instruction-Tuned LLM [Part 1]
Prompt Engineering Best Practices for Instruction-Tuned LLM [Part 2]
Prompt Engineering for Instruction-Tuned LLM: Iterative Prompt Development
Prompt Engineering for Instruction-Tuned LLM: Text Summarization
Prompt Engineering for Instruction-Tuned LLM: Textual Inference & Sentiment Analysis
Prompt Engineering for Instruction-Tuned LLM: Text Transforming & Translation
Prompt Engineering Best Practices: Chain of Thought Reasoning
Prompt Engineering Best Practices: Building an End-to-End Customer Service System
My New E-Book: Prompt Engineering Best Practices for Instruction-Tuned LLM
I am happy to announce that I have published a new ebook Prompt Engineering Best Practices for Instruction-Tuned LLM. Prompt Engineering Best Practices for Instruction-Tuned LLM is a comprehensive guide designed to equip readers with the essential knowledge and tools to master the fine-tuning and prompt engineering of large language models (LLMs). The book covers everything from foundational concepts to advanced applications, making it an invaluable resource for anyone interested in leveraging the full potential of instruction-tuned models.
1. Prompt Engineering Best Practices for Instruction-Tuned LLM [Part 1]
Have you ever wondered why your interaction with a language model falls short of expectations? The answer may lie in the clarity of your instructions.
Picture this scenario: requesting someone, perhaps a bright but task-unaware individual, to write about a popular figure. It’s not just about the subject; clarity extends to specifying the focus — scientific work, personal life, historical role — and even the desired tone, professional or casual. Much like guiding a fresh graduate through the task, offering specific snippets for preparation sets the stage for success.
In this part 1 notebook, we’re going to help you make your talks with the language model better by getting good at giving clear and specific instructions to get the expected output.
2. Prompt Engineering Best Practices for Instruction-Tuned LLM [Part 2]
Have you ever wondered why your interaction with a language model falls short of expectations? The answer may lie in the clarity of your instructions.
Picture this scenario: requesting someone, perhaps a bright but task-unaware individual, to write about a popular figure. It’s not just about the subject; clarity extends to specifying the focus — scientific work, personal life, historical role — and even the desired tone, be it professional or casual. Much like guiding a fresh graduate through the task, offering specific snippets for preparation sets the stage for success.
In this part 2 notebook, we’re going to help you improve your talks with the language model by getting really good at giving clear and specific instructions to get the expected output.
3. Prompt Engineering for Instruction-Tuned LLM: Iterative Prompt Development
When you build applications with large language models, it is difficult to come up with a prompt that you will end up using in the final application on your first attempt.
However as long as you have a good process to iteratively make your prompt better, then you’ll be able to come to something that works well for the task you want to achieve. You may have heard that when training a machine learning model, it rarely works the first time.
Prompting also does not usually work from the first time. In this notebook, we will explore the process of getting to prompts that work for your application through iterative development.