Best of Prompt Engineering — 2025

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Article
DEV·40w
Programming Is Becoming Prompting
The programming landscape is shifting as AI tools transform coding from writing functions to crafting prompts. While AI assistance can scaffold codebases, generate tests, and speed up routine tasks, it risks diminishing creativity and problem-solving skills. Developers need to balance leveraging AI for efficiency while maintaining deep coding knowledge for debugging, scaling, and handling complex edge cases. The key is knowing when to use AI and when to code manually, as understanding fundamentals remains crucial when AI-generated solutions break or need customization.
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Article
WebCraft·46w
prompts.chat
A directory website that curates and organizes AI prompts for various use cases. The platform serves as a resource for finding pre-written prompts to use with AI language models like ChatGPT and other LLMs.
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Video
Tiff In Tech·47w
10 High-Paying Tech Skills That Will Dominate the Next Decade
Explores 10 emerging high-paying tech skills beyond traditional AI and development roles. Covers quantum computing applications in traffic optimization, GIS for spatial data analysis, creative technology for immersive experiences, prompt engineering for AI communication, service-oriented architecture for scalable systems, facilities tech integration for smart buildings, low-code development platforms, digital twin simulations, edge computing for real-time processing, and ethical hacking for security testing. Each skill includes real-world examples and learning resources.
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Article
Machine Learning Mastery·1y
7 Next-Generation Prompt Engineering Techniques
Mastering prompt engineering is essential in optimizing large language models like ChatGPT and Gemini. Techniques such as meta prompting, least-to-most prompting, multi-task prompting, role prompting, task-specific prompting, program-aided language models, and chain-of-verification prompting can significantly enhance the performance and efficiency of LLMs. Each method has unique benefits and challenges, but collectively, they improve the accuracy and relevance of the generated content.
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Article
controversy.dev·41w
Enough is enough. Prompt engineering is not engineering.
Argues that prompt engineering is fundamentally different from traditional software engineering, lacking the systematic design, mathematical rigor, and testable logic that define real engineering disciplines. The author contends that calling prompt writing 'engineering' is misleading marketing that inflates the perceived technical complexity of working with AI language models.
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Article
Neon·1y
Prompt Engineering as a Developer Discipline
Structured prompting is becoming a crucial skill for developers, akin to traditional coding practices. Using AI effectively involves treating prompts as modular, testable components within software systems. Techniques like few-shot prompting, chain-of-thought reasoning, self-consistency, skeleton prompting, and configuration parameters improve AI's coding outputs. Developers should rigorously validate and maintain prompts, just like any other code, to ensure reliability and consistency in AI-powered features.
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Article
LangChain·44w
How to Build an Agent
A comprehensive framework for building AI agents from concept to production, covering six key steps: defining realistic tasks with concrete examples, creating standard operating procedures, building an MVP with focused prompts, connecting to real data sources, testing and iteration, and deployment with continuous refinement. The guide emphasizes starting small with well-scoped problems, focusing on core LLM reasoning tasks first, and treating deployment as the beginning of iteration rather than the end of development.
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Article
Simon Willison·30w
Claude Skills are awesome, maybe a bigger deal than MCP
Anthropic introduced Claude Skills, a new pattern for extending LLM capabilities using Markdown files with instructions, scripts, and resources. Skills are token-efficient (loading only when needed), depend on code execution environments, and are simpler to create than MCP implementations. The system enables general computer automation beyond just coding tasks, with skills shareable as single files or folders. Skills work with other models too, potentially sparking wider adoption than the Model Context Protocol.
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Article
Daily Dose of Data Science | Avi Chawla | Substack·20w
The AI Engineering Guidebook
A comprehensive 350+ page guidebook covering the engineering fundamentals of LLM systems, including model architecture, training, prompt engineering, RAG systems, fine-tuning techniques like LoRA, AI agents, Model Context Protocol, optimization strategies, and deployment considerations. The resource focuses on practical engineering decisions, system design tradeoffs, and real-world implementation patterns rather than surface-level usage.
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Article
Tiger's Place·1y
Prompt Engineering Tips
Learn essential tips for prompt engineering, including clear communication, providing detailed instructions, structuring requests, handling edge cases, and iterative refinement. Understand the importance of testing prompts, considering user behavior, and ongoing learning through practice and experimentation.
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Article
Javarevisited·43w
Top 5 Books to Learn Prompt Engineering in 2025
A curated list of five essential books for learning prompt engineering in 2025, covering topics from foundational principles to advanced applications. The selection includes practical guides for developers building LLM applications, comprehensive resources on AI engineering infrastructure, specialized books for educational applications, and career-focused materials. Each book targets different audiences from beginners to experienced practitioners, with emphasis on real-world implementation, ethical considerations, and industry best practices.
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Article
LangChain·46w
The rise of "context engineering"
Context engineering is emerging as a critical skill for AI engineers, focusing on building dynamic systems that provide LLMs with the right information, tools, and formatting to accomplish tasks reliably. Unlike traditional prompt engineering, context engineering emphasizes providing complete, structured context rather than clever wording. The approach addresses the primary cause of agent failures: inadequate context rather than model limitations. Key components include dynamic information retrieval, appropriate tool selection, proper formatting, and comprehensive system design. LangGraph and LangSmith are positioned as enabling technologies for implementing effective context engineering practices.
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Article
Medium·42w
The Open Source Project That Became an Essential Library for Modern AI Engineering
A GitHub repository collecting system prompts from AI tools has grown from 12,000 to 70,000 stars, becoming a collaborative library for understanding AI behavior. System prompts are configuration files that define AI model behavior, personality, and ethical boundaries before user interaction. The project provides transparency into how popular AI tools like Cursor work, but raises dual-use concerns as the same information could help both developers build better AI and malicious actors bypass safety features. The author advocates for transparency over security through obscurity, believing an informed community is the best defense. Future plans include better organization, quality control, and expanded security resources.
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Article
Hacker News·45w
The New Skill in AI is Not Prompting, It's Context Engineering
Context Engineering emerges as a more comprehensive approach than prompt engineering for building effective AI agents. Rather than focusing solely on crafting perfect prompts, it involves designing dynamic systems that provide LLMs with the right information, tools, and format at the right time. The concept encompasses system prompts, user inputs, conversation history, long-term memory, retrieved information (RAG), available tools, and structured outputs. The key difference between basic and sophisticated AI agents lies not in code complexity but in context quality - successful agents gather comprehensive contextual information before generating responses, while failures often stem from inadequate context rather than model limitations.
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Article
Javarevisited·42w
Top 5 Books to Learn LLMs (Large Language Models) in Depth
A curated list of five essential books for learning Large Language Models in depth, covering everything from basic engineering concepts to production deployment. The recommendations include practical guides for building LLM applications, training models from scratch, and deploying them at scale. Each book targets different aspects of LLM development, from foundational architecture and prompt engineering to production monitoring and evaluation strategies.
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Article
Community Picks·46w
jujumilk3/leaked-system-prompts: Collection of leaked system prompts
A GitHub repository collecting leaked system prompts from popular LLM-based services. The project accepts contributions through pull requests with verifiable sources and reproducible prompts, while avoiding sensitive commercial code to prevent DMCA takedowns. The repository serves as a research resource cited in academic papers.
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Article
Hacker News·40w
gpt-5 leaked system prompt
A leaked system prompt reveals GPT-5's internal instructions and capabilities. The prompt shows personality guidelines emphasizing clarity and enthusiasm, memory management through a 'bio' tool, canvas functionality for document creation, image generation capabilities, Python code execution environment, and web search tools. It includes specific behavioral constraints like avoiding opt-in questions and copyright material reproduction.
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Article
Reinier·41w
Cursor AI Complete Guide (2025): Real Experiences, Pro Tips, MCPs, Rules & Context Engineering
A comprehensive guide covering Cursor AI, an AI-powered code editor, including setup instructions, advanced features like Model Context Protocols (MCPs), configuration rules, and context engineering techniques. The guide includes real-world experiences and professional tips for maximizing productivity with AI-assisted development, plus a practical example of building an AI SaaS application for automated newsletter generation.
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Article
Medium·38w
5 Agent Workflows You Need to Master (And Exactly How to Use Them)
Five structured AI agent workflows are presented to replace ad-hoc prompting: prompt chaining breaks complex tasks into sequential steps, routing directs queries to appropriate models based on complexity, parallelization runs independent tasks simultaneously, orchestrator-workers use a planning model to coordinate specialized workers, and evaluator-optimizer creates feedback loops for quality improvement. Each workflow includes Python code examples and addresses specific use cases like code generation, content creation, and data analysis to achieve more consistent and production-ready results.
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Article
Javarevisited·41w
Top 5 Udemy Courses to Learn Claude Code and Claude AI in 2025
Claude AI and Claude Code are emerging as powerful tools in the AI development stack, created by Anthropic with a focus on safety and natural language understanding. Claude Code enables developers to write production-ready code through conversational prompts and automate workflows with AI agents. The article curates five Udemy courses covering different aspects: from basic Claude Code usage and full-stack AI development to advanced agent building with frameworks like LangChain, CrewAI, and AutoGen. These courses cater to various skill levels and use cases, from beginners learning AI-assisted coding to experienced developers building complex autonomous agents.
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Article
ByteByteGo·34w
How Anthropic Built a Multi-Agent Research System
Anthropic built a multi-agent research system using an orchestrator-worker pattern with a Lead Researcher agent coordinating specialized subagents and a Citation Agent for accuracy. The system outperformed single-agent setups by 90% through parallel processing and dynamic adaptation, though it consumes 15x more tokens. Key engineering principles include proper prompt design, delegation strategies, effort scaling, and parallelization. Production challenges involve managing stateful agents, debugging non-deterministic behavior, and handling deployments without breaking running tasks.
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Article
Atomic Spin·49w
Tips & Tricks for Better AI Prompts
Effective AI prompt engineering requires structured formatting with clear sections like Instructions, Context, and Examples. Keep prompts concise rather than verbose, as AI models excel at inferring from limited examples. Using markdown-style sectioning and asking AI to help craft system prompts can significantly improve response accuracy and reliability.
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Article
Reid Burke·28w
claude-cookbooks/coding/prompting_for_frontend_aesthetics.ipynb at 293cde3d3fe1e29ce90b535ccfd311c289302d0c · anthropics/claude-cookbooks
A Jupyter notebook cookbook from Anthropic demonstrating techniques for prompting Claude to generate aesthetically pleasing frontend code. Part of a collection showcasing effective ways to use Claude for coding tasks, specifically focused on improving visual design outcomes through better prompting strategies.
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Article
Daily Dose of Data Science | Avi Chawla | Substack·38w
JSON prompting for LLMs
JSON prompting improves LLM outputs by providing structured format instead of vague natural language instructions. This technique leverages AI models' training on structured data from APIs and web applications, resulting in more consistent and predictable responses. JSON prompts eliminate ambiguity, enable output control, and create reusable templates for scalable AI workflows. While JSON is effective, alternatives like XML for Claude and Markdown also work well - the key is structure rather than specific syntax.
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Video
YouTube·48w
99% Of People STILL Don't Know The Basics Of Prompting (ChatGPT, Gemini, Claude)
Effective AI prompting requires strategic thinking rather than simple commands. The key principles include first principles thinking to break down complex problems into core components, chain of thought prompting to build context through layered interactions, and meta-prompting to collaborate with AI in designing better prompts. Most people treat AI like a search engine, but mastering prompting as a thinking discipline involves defining clear outcomes, providing proper context, and structuring requests systematically.
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