If-Then Text Rules

Create conditional text transformation rules with if-then logic. Automate text processing with custom conditions and actions for efficient content management.

If-Then Text Rules

Create conditional rules to transform text based on specific conditions

Input Text

Enter the text you want to process with rules

Characters: 0Words: 0

Output Text

Processed text with rules applied

If-Then Rules
0 rules

Create conditional rules to transform your text

IF Condition

THEN Action

Understanding Conditional Text Processing

Conditional text processing uses if-then logic to automatically transform text based on specific criteria. This approach enables sophisticated text automation, content standardization, and dynamic text generation based on contextual rules and conditions.

Core Concepts

  • Conditions: Boolean expressions that evaluate text properties
  • Actions: Text transformations applied when conditions are met
  • Rule chains: Sequential application of multiple rules
  • Priority systems: Ordered execution of competing rules
  • Context awareness: Rules that consider surrounding text
  • Pattern matching: Regular expressions and string matching
  • State management: Maintaining context across transformations
  • Error handling: Graceful degradation when rules fail

Rule Types

  • Simple replacement: Direct word/phrase substitution
  • Conditional formatting: Style changes based on content
  • Length-based rules: Actions triggered by text length
  • Position-dependent: Rules based on text location
  • Content-aware: Rules that analyze semantic meaning
  • Pattern-based: RegEx-driven transformations
  • Statistical rules: Frequency and distribution-based
  • Meta-rules: Rules that modify other rules

Rule Condition Types and Operators

Text Matching Conditions

Exact Matching

equals: text exactly matches "value"
not equals: text doesnt match "value"
equals ignore case: case-insensitive match
in list: text matches any item in list
not in list: text matches no items in list

Partial Matching

contains: text includes substring
not contains: text excludes substring
starts with: text begins with prefix
ends with: text concludes with suffix
matches pattern: RegEx pattern match

Advanced Matching

fuzzy match: approximate string matching
soundex: phonetic similarity matching
metaphone: advanced phonetic matching
edit distance: Levenshtein distance threshold
semantic similarity: meaning-based matching

Numerical and Length Conditions

Length-Based

length equals: character count matches value
length greater than: longer than threshold
length less than: shorter than threshold
word count equals: specific word count
word count range: between min and max words
line count: number of lines condition

Numerical Content

is number: text represents a number
number equals: numeric value matches
number greater than: value exceeds threshold
number in range: value within bounds
is integer: whole number validation
is decimal: decimal number validation

Position and Context Conditions

Position-Based

is first word: appears at text beginning
is last word: appears at text end
line number: specific line position
word position: nth word in sequence
paragraph position: location within paragraph

Context-Aware

preceded by: previous word matches
followed by: next word matches
surrounded by: between specific words
in sentence with: same sentence contains
in paragraph with: same paragraph contains

Format Detection

is uppercase: all capital letters
is lowercase: all lowercase letters
is title case: proper capitalization
is camel case: camelCase format
has punctuation: contains punctuation marks

Action Types and Transformations

Basic Text Actions

Replacement Actions

Replace with: Direct text substitution
Replace with emoji: Convert to emoji symbol
Replace with variable: Dynamic content insertion
Replace with function: Computed replacement
Remove text: Delete matched content
Duplicate text: Repeat the matched text

Format Actions

To uppercase: Convert to CAPS
To lowercase: Convert to lowercase
To title case: Proper Case Format
To sentence case: First letter capital
To camel case: camelCaseFormat
To snake case: snake_case_format

Enhancement Actions

Add prefix: Prepend text before
Add suffix: Append text after
Wrap with: Surround with markers
Bold text: **bold formatting**
Italic text: *italic formatting*
Highlight: ==highlighted text==

Advanced Actions

Computational Actions

Calculate: Perform mathematical operations
Count occurrences: Replace with frequency count
Generate hash: Create unique identifier
Timestamp: Add current date/time
Random value: Insert random content
Sequence number: Auto-incrementing numbers

Conditional Actions

If-else chain: Multiple condition branches
Switch statement: Multiple value mapping
Lookup table: Dictionary-based replacement
Template engine: Variable substitution
Script execution: Custom code execution
API call: External service integration

Common Use Cases and Examples

Content Standardization

Business Rules

IF word equals "YES" THEN replace with "✅"
IF word equals "NO" THEN replace with "❌"
IF contains "urgent" THEN add prefix "🚨 "
IF contains "completed" THEN add suffix " ✓"
IF length > 50 THEN add suffix "..."

Format Consistency

IF matches phone pattern THEN format as (XXX) XXX-XXXX
IF is date THEN format as YYYY-MM-DD
IF is email THEN convert to lowercase
IF is URL THEN ensure https:// prefix
IF is currency THEN format with $ symbol

Content Enhancement

Automated Formatting

IF word is acronym THEN convert to uppercase
IF is product name THEN make bold
IF is technical term THEN add to glossary
IF is measurement THEN add unit symbol
IF is percentage THEN add % symbol

Dynamic Content

IF contains "[DATE]" THEN replace with current date
IF contains "[USER]" THEN replace with username
IF contains "[RANDOM]" THEN replace with random number
IF contains "[COUNT]" THEN replace with sequence number
IF contains "[TIME]" THEN replace with timestamp

Quality Control

Validation Rules

IF contains profanity THEN replace with "***"
IF length = 0 THEN replace with "[EMPTY]"
IF contains invalid chars THEN remove them
IF exceeds limit THEN truncate and add "..."
IF duplicate content THEN add "(duplicate)" suffix

Compliance Rules

IF contains SSN pattern THEN mask with XXX-XX-XXXX
IF contains credit card THEN mask with ****
IF contains PII THEN redact content
IF contains sensitive data THEN encrypt
IF violates policy THEN flag for review

Professional Applications

📝

Content Management

CMS automation, content standardization, bulk editing, and publishing workflows

🔄

Data Processing

ETL pipelines, data cleansing, format standardization, and quality assurance

🤖

Process Automation

Workflow automation, rule engines, business process management, and smart routing

📊

Report Generation

Dynamic reports, template processing, conditional formatting, and data visualization

🛡️

Security & Compliance

Data masking, PII protection, content filtering, and regulatory compliance

🎯

Marketing Automation

Personalized content, A/B testing, campaign optimization, and customer segmentation

Implementation Best Practices

✅ Effective Rule Design

  • Design rules with clear, specific conditions to avoid ambiguity
  • Test rules thoroughly with diverse input samples
  • Implement rule priority systems for conflict resolution
  • Use descriptive names and documentation for each rule
  • Monitor rule performance and effectiveness metrics
  • Implement logging and audit trails for rule execution
  • Create fallback rules for edge cases and errors
  • Regular review and optimization of rule sets

❌ Common Pitfalls

  • Overly complex conditions that are hard to maintain
  • Circular dependencies between rules causing infinite loops
  • Insufficient testing leading to unexpected transformations
  • Poor rule ordering causing incorrect precedence
  • Hardcoded values instead of configurable parameters
  • Lack of error handling for malformed input data
  • Missing validation for rule conflicts and contradictions
  • No rollback mechanism for incorrect transformations

Advanced Rule Engine Features

Machine Learning Integration

Adaptive Rules

• Self-learning rule parameters based on user feedback
• Automatic rule generation from training data
• Confidence scoring for rule application decisions
• A/B testing for rule effectiveness comparison

Pattern Recognition

• Natural language processing for semantic rules
• Entity recognition for context-aware processing
• Sentiment analysis for tone-based transformations
• Topic modeling for content categorization

Scalability and Performance

Optimization Techniques

Rule compilation and caching
Parallel rule execution
Lazy evaluation strategies
Index-based condition matching

Distributed Processing

Horizontal rule engine scaling
Load balancing for rule execution
Distributed rule state management
Fault tolerance and recovery

Monitoring & Analytics

Real-time performance metrics
Rule execution statistics
Error tracking and alerting
Business impact measurement

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