Reasoning and constraints
Examples that test multi-step reasoning, hidden assumptions, and verifiable answers.
24 curated prompts
Study practical prompt engineering examples organized by capability and use case. This page highlights reusable patterns such as explicit constraints, structured output, evidence boundaries, scoring rubrics, and task decomposition, helping teams move from one-off prompts to repeatable evaluation-ready templates.
Examples that test multi-step reasoning, hidden assumptions, and verifiable answers.
Prompts that ask for tables, rubrics, extraction, or decision-ready summaries.
Examples for agents, tools, safety checks, and product growth tasks.
Copy-ready prompts selected from this topic cluster.
Prompt from prompts.chat: Skill Maintenance Audit
Prompt from prompts.chat: Job Posting Snapshot & Preservation Engine
Prompt from prompts: Content Calibration Architect
Optimize existing AI coding-agent harnesses (e.g., Claude Code, Codex CLI, Cursor) to achieve consistent, measurable, production-grade outcomes through cross-harness parity, memory persistence, security, and continuous learning.
Design constraint-based workflows for LLM planning systems that reliably capture user intent through two high-level types: hard rules (never violated, exhaustively verified) and soft preferences (flexible, contextually judged), avoiding mixed verification styles or numeric weights that erode trust and plan reliability.
Prompt from prompts.chat: GitHub Actions Workflow Security Reviewer
Prompt from prompts.chat: Act as a Home Espresso Dialing-In Coach
Prompt from prompts.chat: Customizable Job Scanner
Prompt from prompts.chat: TODO Comment Debt Tracker
Prompt from prompts.chat: Act as a Kids' Science Fair Project Mentor
It is a concrete reusable instruction that shows how to define context, constraints, output format, and evaluation criteria for an AI model.
Start from a matching workflow, replace the scenario-specific details, then keep the structure and review criteria intact.
Yes. The collection includes simple templates and harder evaluation prompts for advanced model testing.