# Think in systems Prompt Pack Prompts that push the agent toward architecture, tradeoffs, and production-safe structure instead of surface fixes. Pack URL: https://aicodingguild.com/prompts/packs/think-in-systems Filtered library URL: https://aicodingguild.com/prompts?q=architecture Plain-text export: https://aicodingguild.com/prompts/packs/think-in-systems/txt Featured prompt: Choosing Your Tech Stack — A Decision Framework Prompt count: 7 ## 1. Frontend vs Backend — What's the Difference and Why Does It Matter? Category: Foundations for AI-Assisted Builders Path: Foundations for AI-Assisted Builders Lesson: https://aicodingguild.com/learn/foundations/frontend-vs-backend Prompt page: https://aicodingguild.com/prompts/frontend-vs-backend Tags: frontend, backend, architecture, fundamentals Summary: Understand the two halves of every web application using a restaurant analogy that makes it click. Prompt: "Before implementing this feature, split the work into frontend and backend responsibilities. 1. Explain what the user sees and interacts with 2. Explain what the server must handle behind the scenes 3. List the files or systems likely to change on each side 4. Call out any data that must never live in the frontend 5. Stop before changing auth, permissions, or the data model without my approval I want a clean boundary, not a blurry full-stack mess." ## 2. Choosing Your Tech Stack — A Decision Framework Category: Foundations for AI-Assisted Builders Path: Foundations for AI-Assisted Builders Lesson: https://aicodingguild.com/learn/foundations/choosing-your-tech-stack Prompt page: https://aicodingguild.com/prompts/choosing-your-tech-stack Tags: tech-stack, architecture, decisions, tools Summary: A practical framework for choosing the right tools and technologies for your project — with sensible defaults for AI-assisted builders. Prompt: "Recommend a tech stack for this project. Project type: [describe it] Constraints: [budget, hosting, mobile, data, auth, payments, privacy] My experience level: [describe it] Give me: 1. the default stack you recommend 2. why each tool is a good fit 3. which choices are boring and proven 4. which parts I should avoid over-optimizing right now" ## 3. Java — The Enterprise Workhorse Category: Foundations for AI-Assisted Builders Path: Foundations for AI-Assisted Builders Lesson: https://aicodingguild.com/learn/foundations/java-enterprise Prompt page: https://aicodingguild.com/prompts/java-enterprise Tags: java, spring-boot, jvm, enterprise, programming-languages Summary: Understand where Java lives in the modern tech landscape — Spring Boot, the JVM ecosystem, and why enterprises run on it. Prompt: "Explain this Java or Spring Boot example in terms I can use as a JavaScript or TypeScript developer. 1. Tell me what the code is doing conceptually 2. Map the Java concepts to familiar web concepts in my stack 3. Point out the route, validation, service, and persistence layers 4. Tell me what assumptions an enterprise team is optimizing for here 5. Do not focus on syntax trivia until the architecture is clear Here is the Java example: [paste code or docs]." ## 4. PHP — Still Running the Internet Category: Foundations for AI-Assisted Builders Path: Foundations for AI-Assisted Builders Lesson: https://aicodingguild.com/learn/foundations/php-still-running Prompt page: https://aicodingguild.com/prompts/php-still-running Tags: php, wordpress, laravel, legacy, web-development Summary: Learn why PHP powers 40% of the web — WordPress, Laravel, and the legacy codebase reality that every developer should understand. Prompt: "Help me understand this PHP or WordPress system pragmatically. 1. Tell me whether I am looking at Laravel, WordPress, or custom legacy PHP 2. Explain the overall architecture and where I should start reading 3. Map the main concepts to frameworks and tools I already know 4. Call out version, plugin, security, and maintenance risks 5. Stop before recommending a rewrite unless the current system is truly unsafe or unmaintainable Optimize for realistic maintenance and integration decisions." ## 5. Claude Code — When You Want AI in Your Terminal Category: Working With AI Tools Path: Working With AI Tools Lesson: https://aicodingguild.com/learn/ai-tools/claude-code Prompt page: https://aicodingguild.com/prompts/claude-code Tags: ai-tools, claude-code, terminal, anthropic, advanced Summary: Use Claude Code for terminal-based AI development with deep codebase understanding. Prompt: "I'm using Claude Code on this project. First, read the codebase and summarize: 1. what the app does 2. how the architecture is organized 3. the risky areas to avoid touching casually Then propose a `CLAUDE.md` file and a safe plan for this change: [describe change]. Do not modify [list protected areas] without asking first." ## 6. Chain-of-Thought — Making AI Think Step by Step Category: Working With AI Tools Path: Working With AI Tools Lesson: https://aicodingguild.com/learn/ai-tools/chain-of-thought Prompt page: https://aicodingguild.com/prompts/chain-of-thought Tags: ai-tools, prompt-engineering, chain-of-thought, reasoning Summary: Use chain-of-thought prompting to get AI to reason through complex coding problems. Prompt: **Use this for tasks with real logic, architecture, or debugging complexity:** "I need help with [problem]. Before writing code, work in three phases. Phase 1: Think - Explain the problem in plain English - Identify the main rules, dependencies, and edge cases - Point out anything ambiguous or risky Phase 2: Plan - Propose the implementation approach - List files or systems likely to change - Explain how you will verify correctness Phase 3: Code - Do not start this phase until I approve phases 1 and 2 Keep these constraints in mind: [constraints]. Tell me what I must test manually after implementation." ## 7. OpenAI vs Anthropic vs Open Source — An Honest Comparison Category: Working With AI Tools Path: Working With AI Tools Lesson: https://aicodingguild.com/learn/ai-tools/openai-vs-anthropic-vs-open-source Prompt page: https://aicodingguild.com/prompts/openai-vs-anthropic-vs-open-source Tags: openai, anthropic, open-source, llama, comparison, ai-models Summary: Compare the major AI model providers — strengths, weaknesses, pricing, and when to use which. Prompt: "Help me choose an AI provider strategy for my work. Stack: [describe stack] Task mix: [quick edits, debugging, architecture, code review, private code] Constraints: [budget, privacy, offline needs, existing subscriptions] Recommend: 1. the best default provider 2. when to switch to another provider or model family 3. where open source is worth considering 4. the biggest tradeoffs I should test myself instead of trusting benchmarks"