AI Prompt Template Library
Browse and copy curated AI prompt templates for coding, writing, DevOps, analysis and more. Each prompt includes variable placeholders you can customize. Works with ChatGPT, Claude, Gemini and any LLM.
What is an AI Prompt Template Library?
An AI prompt template library is a curated set of reusable instructions designed to get consistent, high-quality outputs from large language models. Rather than typing a vague request and iterating through poorly formatted responses, a well-crafted template specifies the role, context, format and constraints upfront — so the model delivers a usable result on the first attempt. This library is focused on tasks that come up repeatedly in DevOps, SRE and engineering work: writing runbooks, drafting incident post-mortems, generating Dockerfiles, reviewing pull requests and more.
Prompt engineering is quickly becoming a core skill for engineers who work with AI tools. The difference between a generic prompt ("explain this error") and a structured template ("act as a senior SRE, diagnose this error, and list the three most likely root causes in order of probability") is dramatic. This library gives you production-ready starting points that have been structured to elicit specific, actionable outputs.
When to Use This Tool
- Incident response: Use structured prompts to quickly draft an incident timeline, severity assessment or communication to stakeholders while the incident is still ongoing.
- Runbook generation: Generate a step-by-step runbook for a service or process by providing the system context — the model fills in the operational detail.
- Code and PR review: Feed a diff into a code-review prompt to get a structured review covering correctness, security and performance in seconds.
- Architecture decisions: Use decision-framework prompts to evaluate trade-offs between technology choices with a consistent format every time.
- Team communication: Draft post-mortems, RCAs, release notes and status updates faster with templates that enforce the right structure.
How It Works
Each prompt template uses [VARIABLE] placeholders that you replace with your specific context — the service name, error message, cloud provider or other details. The templates are model-agnostic: they work with ChatGPT, Claude, Gemini, Llama and any other instruction-following LLM. Click a card to expand it, copy the full prompt, substitute your values and paste it directly into your preferred AI chat interface or API.
Frequently Asked Questions
What AI models do these prompts work with?
All prompts are designed to be model-agnostic and have been tested with ChatGPT (GPT-4o), Claude (Sonnet, Opus), Google Gemini and open-source models like Llama and Mistral. They use standard natural language instruction patterns — no model-specific syntax or API features — so they transfer across models without modification. For best results with very large or complex prompts, use a frontier model like GPT-4o or Claude Opus.
How do I customise these prompt templates?
Every template contains [VARIABLE] placeholders highlighted in green. Replace each placeholder with your specific details before sending — for example, replace [SERVICE_NAME] with the actual name of your service, or [ERROR_MESSAGE] with the exact error text. You can also chain prompts: use the output of one template (e.g. a root cause analysis) as the input context for the next (e.g. a post-mortem draft), building a multi-step workflow.
What makes a good DevOps prompt template?
The most effective DevOps prompts share four elements: a clear role assignment ("Act as a senior SRE"), a specific task with a defined output format ("write a numbered runbook"), concrete context placeholders for the details that change each time, and explicit constraints on the response ("under 500 words", "in YAML format", "no markdown"). Prompts that define all four elements consistently produce structured, directly usable outputs rather than verbose, generic responses that require heavy editing.