DGNTool
AI ToolsPrompt Engineering

AI Master Prompt Architect (ChatGPT & Claude)

Construct elite LLM system prompts using the CO-STAR framework with role personas, constraints, and outputs.

Battle-Tested Master Presets
CO-STAR: Role
CO-STAR: Objective

5. Output Format & Tone

Generated Master Prompt

Score:%100
# SYSTEM ROLE & PERSONA You are an elite Staff Software Engineer and Systems Architect with 15+ years of experience in clean architecture, performance optimization, and TypeScript. # CONTEXT & BACKGROUND Production Next.js 14 web application prioritizing privacy, client-side execution, and zero-latency UI. # OBJECTIVE / PRIMARY TASK Analyze the provided code for edge cases, performance bottlenecks, and refactor it with clean, readable modern TypeScript. # CONSTRAINTS & GUIDELINES - Tone & Demeanor: direct - Rules: Avoid conversational filler. Categorize findings by severity (High, Medium, Low) and provide complete, verified drop-in code. - Do not make assumptions beyond the stated requirements. # OUTPUT FORMAT SPECIFICATION Provide a cleanly structured response using Markdown headers (##, ###), concise bullet points, and code blocks where appropriate. --- Now proceed with the requested task following all instructions above.

CO-STAR Framework: Combines Role, Objective, Context, Constraints, and Format to prevent hallucinations.

Overview

Free online system prompt builder for ChatGPT, Claude 3.5, and Gemini. Stop hallucinations with role definition, context framing, negative constraints, and strict output formatting.

100% Private: All operations run locally on your device. Files never touch our servers.

Master System Prompt Engineering Guide

Hallucinations, vague answers, and non-compliant formats occur when LLMs lack structured role framing and negative guardrails.

The CO-STAR framework aligns Context, Objective, Style, Tone, Audience, and Response formats to eliminate ambiguity.

Our architect tool builds copy-ready master instructions compatible with ChatGPT, Claude 3.5/3.7, and Google Gemini.

How to Engineer High-Precision System Prompts

Fast & Intuitive
1

Establish Expert Persona

Define the domain seniority, authoritative stance, and mental models for the AI.

2

Provide Context & Mission

Feed background constraints, project parameters, and clear objective criteria.

3

Enforce Format & Guardrails

Select schema requirements and export directly to ChatGPT or Claude.

Key Highlights & Advantages

100% Client-Side Privacy

Zero server uploads. Everything processes securely within your local browser memory.

Instant Zero-Latency Execution

Immediate results with no file upload or download queues.

Unlimited & Completely Free

No registration, no paywalls, and no hidden quotas.

Cross-Device Responsive Experience

Seamlessly optimized for mobile smartphones, tablets, and desktop workstations.

Modern In-Browser Execution Architecture

All data processing runs natively via W3C compliant browser hardware acceleration.

Expert Tips & Best Practices
  • Always specify negative constraints (e.g. "Do not use generic buzzwords") to keep outputs sharp and professional.
  • For technical tasks, enforce code verification steps within the prompt instructions.

Related Guides

Prompt Engineering & LLM Selection Guide (2026 Edition): Architecture, Benchmarks & Best Practices
Master modern prompt engineering and choose the optimal AI model for your workload. Compare GPT-6 Astra, Claude Opus 5, Gemini 3.8 Flash, Grok 4.6, and DeepSeek-V4.

Frequently Asked Questions

2 Q&As

What is the CO-STAR prompting methodology?

It is a state-of-the-art framework structuring Context, Objective, Style, Tone, Audience, and Response specifications.

Can I launch directly into external LLMs?

Yes, 1-click buttons allow opening the master prompt inside ChatGPT or Claude instantly.

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