The Ultimate Guide to Prompt Engineering Resources (May 2025)
The field of prompt engineering has evolved tremendously, becoming a crucial skillset for effectively working with AI systems. This curated collection brings together the most valuable, practical, and current resources from leading AI companies and trusted experts. Each resource has been carefully selected for its applicability to both beginners and experienced practitioners looking to enhance their prompt engineering capabilities.
Official Documentation and Guides
OpenAI Resources
OpenAI Prompt Engineering Guide
Source/Author: OpenAI
Updated as recently as April 2025, this comprehensive guide provides practical examples with actual code snippets in multiple languages. It offers fundamental techniques for text generation alongside real-world implementation examples, making it immediately applicable for developers. The guide balances theoretical knowledge with hands-on application, establishing it as an essential starting point for anyone serious about prompt engineering with OpenAI models.
GPT-4.1 Prompting Guide
Source/Author: OpenAI
Released in April 2025, this guide focuses specifically on OpenAI's most advanced model. It contains model-specific optimizations derived from extensive internal testing, making it invaluable for practitioners seeking to leverage GPT-4.1's enhanced capabilities. The guide offers unique insights not found in the general prompt engineering documentation, particularly beneficial for professional applications requiring maximum performance.
Google Resources
What is Prompt Engineering
Source/Author: Google Cloud
Updated May 16, 2025, this is Google's most current introduction to the field. The guide breaks down complex concepts into accessible explanations of key elements that contribute to effective prompt engineering. It particularly excels in explaining structural considerations and providing a framework for thinking about prompts systematically, benefiting users across all experience levels.
Prompt Engineering: Comprehensive Whitepaper
Source/Author: Google
This substantial 68-page whitepaper (September 2024) offers unparalleled depth on prompt engineering techniques. It covers advanced topics like ReAct prompting, automatic prompt engineering, and detailed configuration settings like temperature, top-k, and top-p parameters. The comprehensive approach makes it particularly valuable for practitioners looking to move beyond basics to sophisticated implementation strategies.
Introduction to Prompt Design for Gemini API
Source/Author: Google AI
Published in April 2025, this guide is specifically tailored for Google's Gemini models. It presents model-specific optimization strategies and techniques that work particularly well with Gemini's architecture. For anyone working specifically with Google's AI models, this resource provides targeted guidance that general prompt engineering principles might not cover.
Google Prompting Essentials
Source/Author: Google
This structured learning resource teaches prompt engineering in 5 discrete steps, with a focus on practical application. Unlike static documentation, the course format builds cumulative knowledge, making it particularly effective for beginners. The emphasis on time-saving approaches for complex tasks makes this especially valuable for professionals looking to incorporate AI into their workflows efficiently.
Anthropic Resources
Interactive Prompt Engineering Tutorial
Source/Author: Anthropic
This hands-on tutorial employs an interactive learning approach that dramatically improves retention of prompt engineering concepts specifically for Claude models. The step-by-step structure provides immediate feedback on techniques, making it uniquely effective for experiential learners. Its GitHub format also allows users to fork and customize examples for their specific use cases.
Prompt Engineering Overview
Source/Author: Anthropic
Although from December 2023, this guide retains significant value by clearly articulating when to use prompt engineering versus fine-tuning. It presents a practical decision framework that helps users avoid wasting resources on unnecessarily complex approaches when prompt engineering would suffice. The guide's focused clarity on this specific decision point makes it a valuable reference despite its age.
Perplexity Resources
Prompting Tips and Examples on Perplexity
Source/Author: Perplexity AI
Updated in October 2024, this platform-specific guide excellently categorizes different prompt types and provides practical examples for each. The resource is particularly valuable for its straightforward explanation of informational, instructional, and interactive prompts, along with guidance on how to structure each for optimal results. Its concise format makes it ideal for quick reference during prompt crafting sessions.
Supplementary Resources
Prompt Engineering Guide
Source/Author: Community-maintained resource
Updated as recently as April 2025, this guide provides a discipline-focused approach to prompt engineering with emphasis on wide applicability across multiple LLM platforms. Its comprehensive coverage makes it particularly valuable for practitioners who work across different AI systems and need to understand transferable principles rather than platform-specific techniques.
12 Prompt Engineering Tips for Claude
Source/Author: Vellum AI
While from February 2024, this resource offers uniquely valuable model-specific techniques for Claude users. The practical tips-such as using XML tags, positioning long documents before instructions, and implementing "think step-by-step" methodology-provide immediately applicable strategies that significantly improve Claude's output quality. Each technique is explained with concrete examples that demonstrate clear implementation paths.
Conclusion
These curated resources represent the most practical, accessible, and current guidance on prompt engineering available as of May 2025. For beginners, starting with Google's Prompting Essentials or Perplexity's tips provides an accessible entry point. For more advanced practitioners, the comprehensive guides from OpenAI and Google's whitepaper offer deeper insights into optimizing prompts for specific applications and models.