The 2025 Guide to Prompt Engineering

Welcome to your ultimate resource for mastering prompt engineering in 2025. This comprehensive guide offers a curated collection of tools, tutorials and real-world examples designed to help learners at every level understand and apply effective prompt engineering techniques.

As generative AI continues to reshape industries, the ability to craft precise prompts for AI models—including large language models (LLMs) like OpenAI’s GPT-4, IBM® Granite®, Anthropic’s Claude, Google’s Bard, DALL·E and Stable Diffusion—has become a critical skill. Whether you're working with proprietary systems or exploring open source alternatives, prompt engineering is the key to unlocking the full potential of AI-powered tools.

Prompt engineering is the new coding. In a world increasingly driven by machine learning, the ability to communicate with AI-generated systems by using natural language is essential. This guide will help you design, refine and optimize prompts that drive meaningful results—whether you're building applications, automating workflows or pushing the boundaries of creative expression.

From foundational concepts to advanced strategies, this guide is your go-to reference for navigating the evolving landscape of large language models (LLMs), AI prompt design and generative AI (genAI) innovation.

Crafting better prompts is only the beginning. True expertise in advanced prompting lies in understanding the broader context in which AI models operate—ranging from user intent and conversation history to the structure of training data and the behavior of different models. This is where context engineering becomes essential, enabling you to shape not just what you ask, but how the model interprets and responds.

By leveraging techniques like retrieval-augmented generation (RAG), summarization and structured inputs such as JSON, you can guide models toward more accurate and relevant model responses. Whether you're working on code generation, content creation or data analysis, designing with context ensures alignment with the desired output. This approach enhances the performance of LLMs across tasks and improves the reliability of outputs in real-world applications.

Dive into the Prompt Engineering Guide with a structured path designed for learners, developers and AI enthusiasts. Whether you're building a chatbot, automating complex tasks or experimenting with AI tools, this guide covers everything you need to master the art and science of prompt design.

This guide serves as a foundational resource for understanding and applying prompt engineering across a range of AI-driven applications. For those seeking practical, hands-on experience, the IBM.com Tutorials GitHub Repository offers a collection of real-world use cases and step-by-step implementations by using Python, complete with code snippets and structured workflows. This repository is particularly valuable for learners and practitioners aiming to deepen their expertise in prompt design, model interaction and the broader ecosystem of AI tools