# System prompts that work: shaping the instruction behind [Skip to content](#lm-inhoud)Network/[NL](/en/systeemprompts)EN[Hubhub.llmnet.nlCompare models on task, language, cost and license.](https://hub.llmnet.nl/en/)[Communitycommunity.llmnet.nlPrompt techniques, patterns and system prompts.](https://community.llmnet.nl/en/)[APIapi.llmnet.nlLLMs in production: rate limits, routing, structured output.](https://api.llmnet.nl/en/)[Consultancyconsultancy.llmnet.nlRolling out AI in an organization, pilot to production.](https://consultancy.llmnet.nl/en/)[Newsnieuws.llmnet.nlAI developments, explained for the Netherlands.](https://nieuws.llmnet.nl/en/)[Benchmarkbenchmark.llmnet.nlMeasure AI quality yourself, on your own tasks.](https://benchmark.llmnet.nl/en/)[Careersvacatures.llmnet.nlAI roles, salaries and career paths in the Netherlands.](https://vacatures.llmnet.nl/en/)[Learnleren.llmnet.nlAI concepts in plain language, beginner to builder.](https://leren.llmnet.nl/en/)[Guidegids.llmnet.nlRun AI privately on your own Mac, PC, NAS or home server.](https://gids.llmnet.nl/en/)[Directorydirectory.llmnet.nlMapping the AI ecosystem: tools, models, companies.](https://directory.llmnet.nl/en/)[Radarradar.llmnet.nlSignals from X, research and communities for indie developers.](https://radar.llmnet.nl/en/)[Appsapps.llmnet.nlReviews of AI apps and open-source repos, with tips for builders.](https://apps.llmnet.nl/en/)[llmnet.nl — main site](https://llmnet.nl/en/)[](https://x.com/intent/post?url=https%3A%2F%2Fcommunity.llmnet.nl%2Fen%2Fsysteemprompts&text=System%20prompts%20that%20work%3A%20shaping%20the%20instruction%20behind)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fcommunity.llmnet.nl%2Fen%2Fsysteemprompts)[](https://www.reddit.com/submit?url=https%3A%2F%2Fcommunity.llmnet.nl%2Fen%2Fsysteemprompts&title=System%20prompts%20that%20work%3A%20shaping%20the%20instruction%20behind)[](#)[](https://x.com/intent/post?url=https%3A%2F%2Fcommunity.llmnet.nl%2Fen%2Fsysteemprompts&text=System%20prompts%20that%20work%3A%20shaping%20the%20instruction%20behind)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fcommunity.llmnet.nl%2Fen%2Fsysteemprompts)[](https://www.reddit.com/submit?url=https%3A%2F%2Fcommunity.llmnet.nl%2Fen%2Fsysteemprompts&title=System%20prompts%20that%20work%3A%20shaping%20the%20instruction%20behind)[](#)By Ivo Donker — created with AI assistance (Claude & Gemini) · Last updated: July 27, 2026 [llmnet.nl Community](/) # System prompts that work: shaping the instruction behind your AI By the Developer Team • Reading time: 3 minutes The system prompt is the beating heart of every LLM integration. It determines not only what the model knows, but also its personality, reasoning ability, and—just as importantly—what it absolutely must not do. Especially when building web applications that use [RAG (Retrieval-Augmented Generation) with vector search](https://leren.llmnet.nl/en/), a tight basic instruction is crucial to eliminate hallucinations and guarantee reliable output. ## The anatomy of a strong system prompt A robust system prompt for production environments consists of four essential components: - Role and Identity: Who is the AI? (e.g., "You are a senior security architect"). This activates specific latent space within the model. - Guardrails: What is the scope? If the model has no data, is it allowed to guess or must it ask for clarification? - Tone and Format: Should the output be JSON? Markdown? Professional or informal? - Safety Rules: Strict prohibitions (e.g., "Never display PII data", or "Do not generate external URLs unless provided via the [search API](https://api.llmnet.nl/en/)"). ## Examples: Good vs. Bad ### ❌ The "AI Spam" Prompt Vague, open to interpretation, and prone to hallucinations. You are a helpful assistant. Answer the user's questions about code as well and comprehensively as possible. ### ✅ The Developer Prompt Strict, structured, and focused on a specific task. You are a senior backend developer. Write exclusively Python 3.12+ code. Provide answers directly in code blocks without introductions. Recommend scalable, asynchronous solutions. ## Concrete Templates For structured applications, we recommend a declarative approach, where the prompt is split into clear sections. Use the template below in your code: [ROLE] You are a specialized technical assistant for designing scalable web applications. You communicate in clear, direct, and professional English. [GUARDRAILS & CONTEXT] - Your answers must be based on the provided {context_data}. - If the {context_data} contains insufficient information to answer the question, state this directly. Do not make up data (zero-inference). - Focus on high-performance solutions, taking into account asynchronous processes and efficient memory usage. [SAFETY RULES] 1. NEVER execute commands or scripts that are destructive (e.g., DROP TABLE). 2. Do not share API keys, passwords, or other sensitive credentials. [OUTPUT FORMAT] Provide your answer exclusively in valid Markdown. Use `code blocks` for configurations and tables for comparisons. Do not add closing polite phrases. ## Iteration is key Prompt engineering is not a one-time action. Test your system prompts against edge cases, log the interactions in your application, and tighten the guardrails where the AI takes too much freedom. A predictable LLM starts with a compelling, unambiguous system prompt. © 2026 llmnet.nl Community. 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