🚀 INTENT — AI Prompt Generator

Turn ideas into repeatable, high-quality prompts — designed for creators, engineers, and product teams who need predictable, testable outputs from LLMs (ChatGPT, Gemini, and others).

📈 Introduction

The AI Prompt Generator is a focused prompt-authoring workspace that helps you design before you generate. Intent-first prompts + environment-driven variables produce more consistent, accurate outputs and better downstream automation. This tool helps teams reduce iteration time, enforce safety & schema rules, and scale prompt-based workflows across projects.

🧠 Workflow

1. Clone from a template or start from scratch

   • Select a prebuilt template (summarization, QA, code-gen, persona-driven tasks) or create a new prompt file named for intent-based searchability.

2. Map intent to variables & custom logic constraints

   • Define: user_goal, output_format, tone, length_limit, temperature and validation rules.
   • Add constraints like output_schema, forbidden_terms, max_tokens, and safety_checks to enforce production behaviour.

3. Execute using external LLMs

   • Copy and paste the generated prompts into external LLMs like ChatGPT, Gemini, etc.

4. Satisfied? Reuse and standardize

   • Save the final prompt as a canonical template, tag it for discoverability, and attach example inputs/outputs for regression tests.
   • Reuse the template across projects and environments to ensure reproducibility.

⚡ Features

• Folders & files — nested organization for canonical templates, experiments, and archived versions.
• Multiple environments — development, staging, production envs with overrides (ACTIVE_ENV fields).
• Multiple workspaces — team and personal workspaces for access control and collaboration.
• Predefined & custom snippets — quick-insert snippets for common tasks (regex, extraction, summaries).
• Predefined & custom personas — ship personas (Data Scientist, Legal Counsel) and author custom personas starting with [[persona]].
• Rich editor & preview — Markdown-powered editing, live preview, layout options (single/two-column), and schema validation toggles.
• Template fields, active env fields, global env fields — per-prompt inputs, workspace overrides, and org-wide configs/secrets.

🔥 Prompt Library

Curated prompting techniques grouped for clarity and fast adoption.

🟢 Core / Foundational Prompting Techniques

• Zero-Shot Prompting — ask directly without examples.
• Few-Shot Prompting — provide examples to shape output.
• Instruction-Based Prompting — explicit, imperative instructions.
• Role-Based Prompting — assign personas or roles to control voice.
• Contextual Prompting — include environment/context and constraints.

🟡 Reasoning & Thinking Techniques

• Chain of Thought (CoT) — encourage step-by-step reasoning.
• Tree of Thought (ToT) — explore multiple reasoning branches.
• Step-Back Prompting — abstract before solving.
• Socratic Prompting — iterative questioning to refine reasoning.
• Problem Decomposition — split complex tasks into smaller subtasks.

🟠 Iterative & Self-Improving Techniques

• Critique → Improve — generate, critique, and refine.
• Self-Reflection Prompting — model reviews its output.
• Generate → Evaluate → Refine — explicit multi-pass refinement.
• Self-Consistency — create multiple outputs and select consensus.

🔵 Control, Precision & Reliability Techniques

• Constraint-Based Prompting — enforce hard rules and limits.
• Schema / Format Enforcement — force JSON, tables, or strict outputs.
• Output Length / Style Control — control verbosity and tone.
• Assumption Surfacing — require explicit listing of assumptions.

🟣 Multi-Agent & Advanced Techniques

• Multi-Agent Prompting — simulate collaborating experts.
• Debate / Adversarial Prompting — agent vs agent stress-tests.
• Red Team / Blue Team — attacker/defender cycles.
• Expert Panel Simulation — combine perspectives across roles.

⚫ Meta & Planning Techniques

• Plan-and-Execute — plan steps before execution.
• ReAct (Reason + Act) — interleave reasoning with actions/tooling.
• Reflection + Planning Loop — iterate plan → act → reflect.
• Prompt Chaining — pipe outputs as inputs to next prompt.

🛠️ Multitab Editor

Edit multiple prompts side-by-side. Each tab preserves local state and offers quick actions:

• Run prompt in selected environment
• Save as template
• Insert snippet
• Compare outputs between runs

Keyboard friendly: persistent history, tab-grouping, and workspace-level search.

💡 Keyboard Shortcuts

• Ctrl/Cmd + S — Save
• Ctrl/Cmd + Q — Quit Tab
• Ctrl/Cmd + J — Toggle ScratchPad
• Ctrl/Cmd + 1..9 — Switch to tab 1..9
• Ctrl/Cmd + e — Copy generated output

🔐 Browser-Only Storage & Prompt Copy Behavior

The AI Prompt Generator follows a browser-only, user-controlled model. All prompts are created, edited, and stored locally in your browser.

What gets copied

• The copied output includes exactly what is shown in the generated prompt.
• Nothing is added, removed, masked, or filtered during copying.
• If secrets, credentials, or sensitive text are present in the prompt, they will be copied as-is.

Your responsibility when using external LLMs

When pasting a prompt into external tools like ChatGPT or Gemini:

• You are responsible for reviewing the prompt content before sharing.
• Only include data you are comfortable sending to third-party LLM providers.
• Environment fields and variables behave like regular text once rendered.

Why this design exists

• Ensures full transparency — no hidden logic or silent transformations.
• Gives advanced users complete control over prompt structure and content.
• Supports deterministic, reproducible prompt behavior across tools.

Design principle: what you see is what you copy.

⚠️ External LLM Output Accuracy Disclaimer

The AI Prompt Generator helps you design and structure prompts, but responses generated by external LLMs (such as ChatGPT, Gemini, or others) are not guaranteed to be accurate.

Important limitations of LLM-generated output

• Large Language Models can produce incorrect, outdated, or misleading information.
• Outputs may include hallucinations, false assumptions, or incomplete reasoning.
• Responses reflect patterns in training data, not verified facts or real-world understanding.

Your responsibility as a user

• Always review, validate, and fact-check generated outputs before using them.
• Do not rely on LLM responses for legal, medical, financial, or safety-critical decisions without expert verification.
• Treat generated content as assistive drafts, not authoritative sources.

Why this matters

• Promotes responsible AI usage and informed decision-making.
• Sets correct expectations for accuracy and reliability.
• Builds long-term trust through transparency.

Design principle: prompts guide models — models can still make mistakes.