Custom Prompt Check

Validate, Analyze, and Optimize Your LLM Prompts

What is Custom Prompt Checking?

Custom Prompt Checking is the systematic process of validating, analyzing, and optimizing prompts before deploying them in production LLM applications. It encompasses quality assurance, security validation, and performance testing to ensure prompts behave consistently and safely across different scenarios.

A comprehensive prompt checking framework helps identify potential issues like ambiguity, injection vulnerabilities, hallucination triggers, and inconsistent outputs before they impact end users.

Why Prompt Checking Matters

Security

Detect prompt injection vulnerabilities and prevent malicious inputs from compromising your system.

Quality

Ensure consistent, high-quality outputs that meet your application's requirements.

Performance

Optimize token usage, reduce latency, and improve cost efficiency of your prompts.

Key Components of Prompt Checking

Syntax & Structure Validation

Verify prompt structure, ensure proper formatting, check for template variable completeness, and validate XML/JSON delimiters.

Template Variables Delimiter Check Format Validation

Security Analysis

Scan for potential injection vulnerabilities, detect jailbreak patterns, and identify data leakage risks in prompt templates.

Injection Detection Jailbreak Patterns PII Exposure

Semantic Analysis

Evaluate prompt clarity, detect ambiguity, check for contradictory instructions, and assess overall prompt coherence.

Clarity Score Ambiguity Detection Coherence Check

Performance Metrics

Measure token count, estimate costs, analyze prompt efficiency, and benchmark response times across different inputs.

Token Count Cost Estimation Latency Analysis

Prompt Checking Workflow

flowchart LR A["📝 Write Prompt"] --> B["✅ Validate Structure"] B --> C["🔒 Security Scan"] C --> D["🧠 Semantic Analysis"] D --> E["⚡ Performance Test"] E --> F{"Pass All?"} F -->|Yes| G["🚀 Deploy"] F -->|No| H["🔄 Refine"] H --> A

Implementation Example

Python - Prompt Checker Class

import re
from typing import Dict, List, Optional
import tiktoken

class PromptChecker:
    """Comprehensive prompt validation and analysis tool."""
    
    INJECTION_PATTERNS = [
        r"ignore\s+(all\s+)?previous\s+instructions",
        r"you\s+are\s+now\s+",
        r"pretend\s+(to\s+be|you\s+are)",
        r"system\s*:\s*",
        r"</?system>",
    ]
    
    def __init__(self, model: str = "gpt-4"):
        self.encoder = tiktoken.encoding_for_model(model)
        self.model = model
    
    def check_structure(self, prompt: str) -> Dict:
        """Validate prompt structure and formatting."""
        issues = []
        
        # Check for unmatched delimiters
        delimiters = [(", "), ("<", ">"), ("[", "]")]
        for open_d, close_d in delimiters:
            if prompt.count(open_d) != prompt.count(close_d):
                issues.append(f"Unmatched '{open_d}' delimiter")
        
        # Check for empty sections
        if re.search(r"\n\n\n+", prompt):
            issues.append("Excessive whitespace detected")
        
        return {"valid": len(issues) == 0, "issues": issues}
    
    def check_security(self, prompt: str) -> Dict:
        """Scan for potential security vulnerabilities."""
        vulnerabilities = []
        
        for pattern in self.INJECTION_PATTERNS:
            if re.search(pattern, prompt, re.IGNORECASE):
                vulnerabilities.append({
                    "type": "injection_risk",
                    "pattern": pattern,
                    "severity": "high"
                })
        
        return {
            "secure": len(vulnerabilities) == 0,
            "vulnerabilities": vulnerabilities
        }
    
    def analyze_tokens(self, prompt: str) -> Dict:
        """Analyze token usage and estimate costs."""
        tokens = self.encoder.encode(prompt)
        token_count = len(tokens)
        
        # Cost estimation (example rates)
        cost_per_1k = {"gpt-4": 0.03, "gpt-3.5-turbo": 0.001}
        rate = cost_per_1k.get(self.model, 0.01)
        
        return {
            "token_count": token_count,
            "estimated_cost": (token_count / 1000) * rate,
            "efficiency": "optimal" if token_count < 2000 else "review recommended"
        }
    
    def full_check(self, prompt: str) -> Dict:
        """Run all checks and return comprehensive report."""
        return {
            "structure": self.check_structure(prompt),
            "security": self.check_security(prompt),
            "tokens": self.analyze_tokens(prompt),
            "overall_score": self._calculate_score(prompt)
        }

# Usage Example
checker = PromptChecker(model="gpt-4")
result = checker.full_check(my_prompt)
print(f"Security: {result['security']['secure']}")
print(f"Tokens: {result['tokens']['token_count']}")

Prompt Validation Checklist

Category	Check	Priority
Structure	All template variables are defined	Critical
Structure	Delimiters are properly matched	Critical
Security	No injection vulnerabilities detected	Critical
Security	System prompt is protected from leakage	High
Clarity	Instructions are unambiguous	High
Clarity	Output format is clearly specified	High
Performance	Token count is within budget	Medium
Performance	No redundant instructions	Low

Prompt Checking Tools

Promptfoo

Open-source prompt testing & evaluation

LangSmith

LangChain's prompt debugging & testing

Guardrails AI

Input/output validation framework

NeMo Guardrails

NVIDIA's programmable guardrails

Best Practices

Automate Your Checks

Integrate prompt validation into your CI/CD pipeline to catch issues before deployment.

Version Control Prompts

Treat prompts as code—use version control, code review, and track changes over time.

Test Edge Cases

Build a test suite with adversarial inputs, edge cases, and common attack patterns.

Monitor in Production

Continuously monitor prompt performance and outputs for anomalies and degradation.

Test Your Knowledge

Score 8/10 or higher to pass