Best of Prompt Engineering — July 2025

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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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2
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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3
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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4
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.
109
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5
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.
87
6
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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7
Article
Daily Dose of Data Science | Avi Chawla | Substack·42w
What is Context Engineering?
Context engineering is emerging as a critical skill for AI engineers, focusing on systematically orchestrating context rather than just clever prompting. Unlike traditional prompt engineering that relies on 'magic words', context engineering creates dynamic systems that provide the right information, tools, and format to LLMs. The approach addresses the real bottleneck in AI applications - not model capability, but setting up proper information architecture. Key components include dynamic information flow, smart tool access, memory management (both short-term and long-term), and format optimization. As AI models improve, context quality becomes the limiting factor for application success.
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8
Article
DiamantAI·42w
Why AI Experts Are Moving from Prompt Engineering to Context Engineering
AI system reliability depends more on context engineering than the underlying models themselves. Context engineering involves providing AI systems with relevant conversation history, data, and documents before processing requests, rather than relying solely on individual prompts. This approach explains why some AI applications excel (like context-aware customer service bots that access order details) while others fail (generic response systems). The perceived improvements in AI intelligence often stem from better information architecture and context management rather than advances in the core models.
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9
Article
Product Hunt·44w
PromptForge: The ultimate prompt engineering workbench
PromptForge is an AI prompt engineering workbench that provides tools for crafting, testing, and systematically evaluating prompts. It includes powerful analysis capabilities to help developers optimize their AI prompts through structured testing and evaluation processes.
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10
Article
Daily Dose of Data Science | Avi Chawla | Substack·43w
Prompting vs. RAG vs. Finetuning
A decision framework for choosing between prompt engineering, RAG, and fine-tuning when building LLM applications. The choice depends on two key factors: the amount of external knowledge required and the level of model adaptation needed. RAG works best for custom knowledge bases without behavior changes, fine-tuning modifies model structure and behavior, prompt engineering suffices for basic adjustments, and hybrid approaches combine RAG with fine-tuning for complex requirements.
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Article
Prompt Engineering·43w
Prompt Management Dashboard
Promptzy is a web application for managing and organizing AI prompts with cross-device synchronization via Supabase. It features a free GPT-4.1 powered assistant for prompt generation, supports multiple installation methods including web, mobile app, npm package, and source code deployment. Users can categorize prompts by system prompt, task, image, or video, and sync data across devices using Supabase credentials and a username.
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Article
The New Stack·44w
Context Engineering: Going Beyond Prompt Engineering and RAG
Context engineering is a comprehensive approach to LLM development that goes beyond simple prompt crafting. It involves designing dynamic systems that manage everything an LLM sees before generating responses - including system instructions, conversation history, retrieved documents, tool outputs, and guardrails. Unlike prompt engineering which focuses on crafting individual queries, context engineering treats the entire context window as a curated information environment. It encompasses RAG as one component while addressing broader challenges like token budget management, information positioning, and maintaining consistency across varied inputs. This systematic approach transforms LLMs from basic chatbots into autonomous agents capable of complex reasoning and decision-making.
25
13
Article
LangChain·41w
Deep Agents
Traditional LLM agents that simply call tools in a loop are limited in handling complex, long-term tasks. Deep agents overcome these limitations through four key components: detailed system prompts with examples, planning tools (like todo lists), sub-agents for task decomposition, and file systems for context management. Applications like Claude Code, Deep Research, and Manus demonstrate this architecture's effectiveness. The author introduces an open-source 'deepagents' package that implements these patterns, making it easier to build specialized deep agents for specific domains.
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14
Article
Codemotion·45w
Chain-of-Thought Prompting: the trick to help AI think better
Chain-of-Thought prompting is a technique that improves AI reasoning by asking language models to explain their step-by-step thinking process before providing final answers. Instead of direct responses, this method encourages models to break down complex problems, show their reasoning, and provide transparent explanations. The technique offers higher accuracy on complex problems, better error detection, and more human-like thinking patterns. Key implementation strategies include using phrases like 'let's think step by step', providing guiding questions, using few-shot examples, and decomposing problems into smaller parts. Practical applications span mathematics, logic, decision-making, and data analysis, making AI responses more reliable and interpretable.
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