Prompt Formatter

Clean, format, and structure AI prompts, system instructions, and embedded code blocks without changing the original content.

Input Content
138 lines1,820 characters
Formatted Output
You are a Senior Java Backend Security Architect.

# Role

Perform a comprehensive security review of the following Spring Boot microservice.

---

# Objective

Analyze the implementation and identify security vulnerabilities, configuration issues, and production risks.

---

# Application Context

Application:
Customer Payment Service

Technology Stack:
- Java 21
- Spring Boot
- PostgreSQL
- Redis
- Kafka
- Docker
- Kubernetes

Traffic:
- 1.5 Million Requests / Day

---

# Requirements

- Do NOT modify business logic.
- Focus only on security and configuration issues.
- Explain the root cause of every issue.
- Provide production-ready recommendations.
- Prioritize Critical → High → Medium severity findings.

---

# Spring Configuration (YAML)

```yaml
spring:
  datasource:
    url: jdbc:postgresql://localhost:5432/paymentdb
    username: postgres
    password: secret
redis:
  host: localhost
  port: 6379
logging:
  level:
    root: INFO
```

---

# SQL Query

```sql
SELECT
  *
FROM
  users
WHERE
  email = 'admin@test.com'
  AND password = '123456'
ORDER BY
  created_at DESC;
```

---

# API Response (JSON)

```json
{
  "success": true,
  "customer": {
    "id": 1,
    "name": "John",
    "roles": [
      "ADMIN",
      "USER"
    ]
  },
  "token": "eyJhbGciOi..."
}
```

---

# XML Configuration

```xml
<configuration>
  <server port="8080">
    <cors enabled="true"/>
    <security enabled="false"/>
  </server>
</configuration>
```

---

# Expected Output

Return the response in Markdown using the following structure:

# Executive Summary

## Critical Issues

## High Priority Issues

## Medium Priority Issues

## Recommendations

## Secure Configuration

## Final Risk Score

---

# Variables

Application Name:
{{application_name}}

Environment:
{{environment}}

Reviewer:
{{reviewer_name}}

Current Date:
{{date}}
✓ Content Preserved
✓ Formatting Applied
0 Warnings
38.65 ms
Deterministic Formatting Rules
1. Preserve Original ContentThe formatter improves spacing, indentation, headings, and overall structure without modifying the original words, meaning, or prompt intent.
2. Format Embedded Code BlocksAutomatically detects and formats embedded JSON, SQL, XML, YAML, Markdown, and other supported code blocks while preserving their content.
3. Organize Prompt SectionsNeatly structures common prompt sections such as Role, Objective, Context, Requirements, Constraints, Examples, and Output Format for improved readability.
4. Preserve Invalid Code SafelyIf a code block contains invalid syntax, the formatter preserves the original content and highlights potential formatting issues without changing the code.
Formatting Change Summary
38.65ms
Formatted embedded YAML code block
Formatted embedded SQL code block
Formatted embedded JSON code block
Formatted embedded XML code block
Detected Prompt Components
6 Found
Role & Persona
Task Objective
Constraints & Rules
Template Variables
Output Format
Embedded Code Blocks
Embedded Code: YAML, SQL, JSON, XML
Workflow Guide

How Prompt Formatter Works

Clean, format, and organize AI prompts into structured, readable, and standardized markdown for better AI responses.

  1. Paste Prompt

    Raw Input • < 1s

    Insert raw prompt drafts, system role instructions, or embedded code snippets into the formatter.

  2. Detect Issues

    Auto-Scan • Instant

    Automatically identify structural clutter, unformatted JSON or SQL blocks, and inconsistent spacing.

  3. Format Prompt

    Formatting • < 1s

    Apply clean markdown formatting, clear section headers, code fences, and XML delimiter tags.

  4. Copy Result

    Ready • Instant

    Copy your structured prompt directly to your clipboard for reliable, high-quality AI outputs.

Key Capabilities

Powerful Features

Everything you need to format, clean, and structure AI prompts for reliable outputs.

100% Free Formatting

Format unlimited prompts and system instructions completely free of charge.

No Account Needed

Clean your prompts immediately without sign-up or registration barriers.

Markdown Standardization

Automatically structure prompts with clean headers, bullet points, and code fences.

Auto Syntax Detection

Identify unformatted JSON, SQL, or code blocks embedded inside prompt text.

XML Delimiter Styling

Add clean XML tags (<context>, <instruction>) for better instruction adherence.

Clutter & Space Cleanup

Remove redundant double spaces, messy line breaks, and awkward indentation.

Private In-Browser Run

All formatting operations run locally on your device for absolute data privacy.

Instant Copy Output

Copy cleanly formatted prompts to your clipboard for instant execution in LLMs.

Fully Mobile Ready

Clean layout designed to format prompts on desktop, tablet, or smartphone screens.

Production Standardized

Ensures prompt structures follow industry-standard prompt engineering rules.

Value & Impact

Why Use This Tool?

Discover how formatting AI prompts improves LLM instruction adherence, eliminates formatting errors, and boosts output quality.

Boost LLM Adherence

Standardized markdown structure and clear section headers help AI models follow complex multi-step instructions reliably.

Eliminate Code Syntax Errors

Clean up unformatted JSON, SQL, or code snippets embedded in system prompts to prevent AI parsing mistakes.

Save Formatting Time

Instantly transform messy prompt drafts into production-grade markdown without manual space or delimiter editing.

Improve Prompt Readability

Well-formatted prompts with XML tags and bullet points make prompt templates easy for team members to read and maintain.

Standardize Team Workflows

Establish consistent prompt formatting conventions across developer teams and prompt engineering workflows.

Zero Data Leakage

Format sensitive system prompts and internal guidelines privately in your browser without cloud storage.

FAQ

Frequently Asked Questions

Clear answers on formatting AI prompts, markdown standards, and structural best practices.

Large language models are pre-trained on structured technical documentation and clean code repositories. Presenting your prompt with clear markdown headers, clean bullet points, and code block fences reduces ambiguity, helping the AI distinguish system instructions from reference text and leading to far more accurate responses.
XML tags (like `<context>`, `<instructions>`, and `<rules>`) wrap specific prompt sections with clear structural boundaries. Modern AI models—especially Anthropic Claude and OpenAI GPT-4o—are explicitly trained to parse XML tags, preventing the model from confusing user input with system commands and reducing instruction injection risks.
No. Prompt formatting cleans up structural layout, fixes irregular spacing, formats code blocks, and adds clear section dividers without changing your core instructions or semantic intent. It presents your raw thoughts in an organized layout that LLMs can parse with maximum precision.
Embedded code and JSON data should always be enclosed in triple-backtick markdown fences (e.g., ```json ... ```) with explicit language identifiers. This signals to the AI model that the enclosed text is raw data to analyze rather than instructions to follow, preventing syntax errors in the output.
No. All prompt formatting transformations execute entirely inside your local browser session. Your raw prompt text, corporate guidelines, and code snippets are never stored, logged, or uploaded to any remote server or database.
All major instruction-tuned language models benefit from clean markdown, including ChatGPT (GPT-4o), Anthropic Claude 3.5, Google Gemini 1.5, Meta Llama 3, and DeepSeek. Standardized headers and lists improve response consistency across every provider.
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