Best of Prompt Engineering — June 2025
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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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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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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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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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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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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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Claude's System Prompt Changes Reveal Anthropic's Priorities
Analysis of Claude 4.0's system prompt reveals how Anthropic uses natural language instructions to program chatbot behavior. Key changes include removal of old hotfixes (now handled in training), encouragement of search functionality, expanded artifact use cases, context optimization for coding, and new cybersecurity guardrails. The 23,000-token system prompt consumes 11% of Claude's context window and demonstrates a user-driven development cycle where observed behaviors are first addressed through prompt modifications, then incorporated into model training.
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How and when to build multi-agent systems
Multi-agent systems require careful consideration of when and how to implement them effectively. Context engineering emerges as the most critical challenge, requiring sophisticated strategies to ensure each agent has appropriate context for their tasks. Systems focused on reading tasks (like research) are generally easier to implement than those emphasizing writing tasks, as read actions are more parallelizable and less prone to conflicting outputs. Production reliability requires durable execution, comprehensive debugging tools, and proper evaluation frameworks. Multi-agent architectures work best for breadth-first queries with high parallelization potential and tasks valuable enough to justify increased token costs.
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The Art of Simplicity·49w
Improve your GitHub Copilot prompts with the Prompt Boost extension
Prompt Boost is a VS Code extension that automatically transforms basic prompts into comprehensive, context-rich instructions for GitHub Copilot and other AI coding assistants. The extension enhances simple requests like 'Create a login component' into detailed specifications including technical requirements, best practices, accessibility guidelines, testing requirements, and documentation standards. It can be used through file-based enhancement with .prompt.md files or directly integrated with VS Code Chat, helping developers get better quality code generation from AI tools.
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Faun·46w
Why Prompting is a Core DevOps Skill
Large language models are transforming DevOps by enabling engineers to generate infrastructure code, CI/CD pipelines, and automation scripts through natural language prompts instead of manually writing complex configurations. This shift from "Infrastructure as Code" to "Infrastructure as a Conversation" offers benefits beyond speed, including consistency, accelerated learning, and creative freedom. The author demonstrates how a simple prompt can replace hours of manual work, such as generating a complete GitHub Actions workflow in minutes rather than the traditional multi-hour process of copying, modifying, and debugging YAML files.
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ThePrimeTime·46wClaude 4 System Prompt
Anthropic published system prompts for Claude 4 models, revealing detailed instructions about personality, safety measures, copyright restrictions, and tool usage. The prompts show extensive efforts to prevent list-heavy responses, copyright violations, and harmful content while enabling features like web search with up to 5 queries and thinking blocks using XML markup. Leaked tool prompts reveal additional restrictions around reproducing copyrighted material and specific search behaviors based on query complexity.
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Build Your Own AI Agent in ONE Prompt! | Deep Agent (Full Tutorial & Demo)
Deep Agent by Abacus AI enables users to create fully functional AI applications using just natural language prompts, requiring no coding skills or API keys. The platform can generate complex apps like fitness trainers, contract analyzers, or recipe generators through a single prompt interface. Users can customize their agents through clarifying questions, preview the results, download source code, and deploy to production. The demo shows building a comprehensive fitness app with user profiles, workout plans, progress tracking, and custom illustrations.
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Prompt Engineering Guide - From Beginner to Advanced
A comprehensive guide covering prompt engineering techniques from basic to advanced levels. Explores key concepts like zero-shot, few-shot, and chain-of-thought prompting, along with model settings such as temperature, top-K, and top-P. Demonstrates practical techniques including role prompting, step-back prompting, self-consistency, tree of thoughts, and ReAct (reason and act) frameworks. Includes hands-on examples using Google AI Studio and various AI models, plus best practices for optimizing prompt effectiveness and model performance.
